The merging of neural radiance fields with generative adversarial networks (GANs) can synthesize novel views of objects from latent code (noise). However, the challenge for generative neural radiance fields (NERFs) is that a single multiple layer perceptron (MLP) network represents a scene or object, and the shape and appearance of the generated object are unpredictable, owing to the randomness of latent code. In this paper, we propose a stylized generative radiance field (SGRF) to produce 3D-aware images with explicit control. To achieve this goal, we manipulated the input and output of the MLP in the model to entangle and disentangle label codes into/from the latent code, and incorporated an extra discriminator to differentiate between the class and color mode of the generated object. Based on the labels provided, the model could generate images of prohibited items varying in class, pose, scale, and color mode, thereby significantly increasing the quantity and diversity of images in the dataset. Through a systematic analysis of the results, the method was demonstrated to be effective in improving the detection performance of deep learning algorithms during security screening.
Citation: Jian Liu, Zhen Yu, Wenyu Guo. The 3D-aware image synthesis of prohibited items in the X-ray security inspection by stylized generative radiance fields[J]. Electronic Research Archive, 2024, 32(3): 1801-1821. doi: 10.3934/era.2024082
[1] | Hongwu Zhang, Xiaoju Zhang . Tikhonov-type regularization method for a sideways problem of the time-fractional diffusion equation. AIMS Mathematics, 2021, 6(1): 90-101. doi: 10.3934/math.2021007 |
[2] | El Mostafa Kalmoun, Fatimah Allami . On the existence and stability of minimizers for generalized Tikhonov functionals with general similarity data. AIMS Mathematics, 2021, 6(3): 2764-2777. doi: 10.3934/math.2021169 |
[3] | Mohamed Abdalla . On Hankel transforms of generalized Bessel matrix polynomials. AIMS Mathematics, 2021, 6(6): 6122-6139. doi: 10.3934/math.2021359 |
[4] | Lynda Taleb, Rabah Gherdaoui . Approximation by the heat kernel of the solution to the transport-diffusion equation with the time-dependent diffusion coefficient. AIMS Mathematics, 2025, 10(2): 2392-2412. doi: 10.3934/math.2025111 |
[5] | Xiangtuan Xiong, Wanxia Shi, Xuemin Xue . Determination of three parameters in a time-space fractional diffusion equation. AIMS Mathematics, 2021, 6(6): 5909-5923. doi: 10.3934/math.2021350 |
[6] | Zhenping Li, Xiangtuan Xiong, Jun Li, Jiaqi Hou . A quasi-boundary method for solving an inverse diffraction problem. AIMS Mathematics, 2022, 7(6): 11070-11086. doi: 10.3934/math.2022618 |
[7] | Lei Hu . A weighted online regularization for a fully nonparametric model with heteroscedasticity. AIMS Mathematics, 2023, 8(11): 26991-27008. doi: 10.3934/math.20231381 |
[8] | Zihan Yue, Wei Jiang, Boying Wu, Biao Zhang . A meshless method based on the Laplace transform for multi-term time-space fractional diffusion equation. AIMS Mathematics, 2024, 9(3): 7040-7062. doi: 10.3934/math.2024343 |
[9] | Jiajia Zhao, Zuoliang Xu . Calibration of time-dependent volatility for European options under the fractional Vasicek model. AIMS Mathematics, 2022, 7(6): 11053-11069. doi: 10.3934/math.2022617 |
[10] | F. Z. Geng . Piecewise reproducing kernel-based symmetric collocation approach for linear stationary singularly perturbed problems. AIMS Mathematics, 2020, 5(6): 6020-6029. doi: 10.3934/math.2020385 |
The merging of neural radiance fields with generative adversarial networks (GANs) can synthesize novel views of objects from latent code (noise). However, the challenge for generative neural radiance fields (NERFs) is that a single multiple layer perceptron (MLP) network represents a scene or object, and the shape and appearance of the generated object are unpredictable, owing to the randomness of latent code. In this paper, we propose a stylized generative radiance field (SGRF) to produce 3D-aware images with explicit control. To achieve this goal, we manipulated the input and output of the MLP in the model to entangle and disentangle label codes into/from the latent code, and incorporated an extra discriminator to differentiate between the class and color mode of the generated object. Based on the labels provided, the model could generate images of prohibited items varying in class, pose, scale, and color mode, thereby significantly increasing the quantity and diversity of images in the dataset. Through a systematic analysis of the results, the method was demonstrated to be effective in improving the detection performance of deep learning algorithms during security screening.
In [1], S. Omri and L. T. Rachdi define the Gauss-Weierstrass transform Wν,t associated with the Hankel transform as follows:
Wν,t(f)(x)=122ν+1Γ(μ+1)∫∞0e−x2+y24ttν+1Jν(ixy2t)f(y)y2ν+1dy, | (1.1) |
where Jν(⋅) is the normalized Bessel function defined in (2.2). This integral transform, which generalizes the usual Weierstrass transform [2,3,4], is used to solve the heat equation problem:
{∂tu(x,t)=Bν(u)(x,t),u(x,0)=f(x), |
where the Bessel differential operator is given by
Bν:=d2dx2+2ν+1xddx,ν≥−12. | (1.2) |
The authors in [1] established practical, real inversion formulas for Hankel-type heat diffusion, building on the ideas of Saitoh, Matsuura, Fujiwara, and Yamada [2,3,4,5,6], and utilizing the theory of reproducing kernels [2,3,4].
Recently, many researchers have adapted and applied this same method to various types of Gauss-Weierstrass integral transforms associated with several kinds of differential and difference-differential operators. For instance, Soltani pioneered the exploration of Lp-Fourier multipliers for the Dunkl operator on the real line [7], extremal functions on Sobolev-Dunkl spaces [8], multiplier operators and extremal functions related to the dual Dunkl-Sonine operator [9], and extremal functions on Sturm-Liouville hypergroups [10]. More recently, the same authors examined Dunkl-Weinstein multiplier operators [11]. Additional research was conducted by Dziri and Kroumi [12], as well as by Ghobber and Mejjaoli [13]. For further work related to existing results on inverse problems, some important findings can be found in [14,15,16].
In this work, we consider the space-fractional diffusion equation associated with the Bessel operator, which is given by
{∂tu(x,t)+(−Bν)α/2u(x,t)=0,x≥0,t>0,u(x,0)=ϕ(x),t>0, | (1.3) |
where the parameters ν and α are restricted by the condition ν≥−12, 1≤α≤2, and the space-fractional Bessel operator (−Bν)α/2, which is defined pointwise by the principal value integral [17],
(−Bν)α/2ϕ(x)=cα,νlimε→0+∫∞εϕ(x)−τξνϕ(ξ)ξα+1dξ, | (1.4) |
here, the normalization constant cν,α is given by
cν,α=2α+νΓ(ν+α2+1)Γ(ν+1)|Γ(−α2)|. |
In this work, we introduce the generalized Gauss-Weierstrass transform associated with the Bessel operator by
(Wα,ν,tϕ)(y)=∫∞0Sα,ν(x,y,t)ϕ(x)σν(dx), |
where Sα,ν(x,y,t) is the fractional heat kernel, which will be defined later. For α=2, this integral transform simplifies to the Gauss-Weierstrass transform defined in (1.1). Thus, it can be considered a one-parameter extension of the transform (1.1). The principal motivation for considering the generalized Gauss-Weierstrass integral transform is that for ν=n2−1, it reduces to the ordinary Gauss-Weierstrass transform for radial functions on the Euclidean space Rn. Since the Bessel operator coincides with the radial part of the Laplace operator Δ=∑mi=1∂2i, this transform provides a significant extension. For more details, the reader is referred to the paper [18]. By using the properties of the Fourier-Bessel transform Fν and its connection with the ∗-convolution product (see Section 2), we first show that the transform Wα,ν,t is a one-to-one bounded linear operator from a Sobolev space Hsν into L2ν(0,∞). By the same argument as the standard Gauss-Weierstrass transform, we can assume that the operator W−1α,ν,t is unbounded or that its range is not closed, which causes the ill-posed problem in solving the operator equation
Wα,ν,tϕ=ψ. |
Then, for stable reconstruction of ϕ, some regularization techniques are necessary. The Tikhonov regularization techniques are widely applicable (e.g., Bakushinsky and Goncharsky [19], Baumeister [20], Tikhonov and Arsenin [21], Tikhonov et al. [22]). In our case, the Tikhonov regularization can be stated as follows: For given data ψ∈L2ν(0,∞), we search for a minimizer of a functional given by
Jγ(ϕ)=12‖Wα,ν,tϕ−ψ‖2L2ν(0,∞)+γ2‖ϕ‖2Hsν,ϕ∈Hsν, |
with a parameter γ>0, which is called a regularizing parameter. We show that the above variational problem has a unique solution denoted by Rγ,ψ and called the regularized solution; it is also referred to as the extremal solution by Soltani [7]. The following theorem is the main result of the paper, which provides a real inversion of the generalized Gauss-Weierstrass transform.
Theorem 1.1. Let s>ν+1. For every ϕ∈Hsν and ψ=Wα,ν,t(ϕ), we have:
limγ→0+‖Rγ,ψ−ϕ‖H(s)ν=0. |
Moreover, the set {Rγ,ψ}γ>0 converges uniformly to ϕ as γ→0+.
Our paper is organized as follows:
● Section 2 serves as an introductory section that provides an overview of fundamental concepts. Topics covered include the Fourier-Bessel transform, generalized translation, generalized convolution, fractional Bessel operator, and the space-fractional Bessel diffusion equation, setting the stage for understanding subsequent content.
● Section 3 is devoted to introducing the generalized Gauss-Weierstrass transform and establishing its principal properties.
● Section 4 states the main results of the paper and provides their proofs.
Before revealing our main results, it is essential to establish the groundwork by introducing key notations and collecting pertinent facts about the Bessel operator. This section serves as a primer, elucidating the significance of the Fourier-Bessel transform and the Delsarte translation, which will be pivotal for the subsequent analysis.
The normalized Bessel function is defined as follows:
Jν(x):=Γ(ν+1)(2/x)νJν(x),ν>−1, | (2.1) |
where Γ(⋅) is the Gamma function [23] and Jν(⋅) is the Bessel function of the first kind, see [23, (10.16.9)]. Then
Jν(x)=∞∑k=0(−14x2)k(ν+1)kk!. | (2.2) |
The normalized Bessel function arises as the unique solution to the eigenvalue problem related to the Bessel equation. More precisely, the functions defined as x→Jν(λx) stand as the unique solution to the eigenvalue problem [23, (10.13.5)]
{Bνϕ(x)=−λ2ϕ(x),ϕ(0)=1,ϕ′(0)=0. |
The function Jν(⋅) is an entire analytic function with even symmetry. Notably, there are straightforward special cases that hold:
J−1/2(x)=cosx,J1/2(x)=sinxx. |
We introduce the following notation:
● Lpν(0,∞) (1≤p) represents the Lebesgue space associated with the measure
σν(dx)=x2ν+12νΓ(ν+1)dx. | (2.3) |
The norm ‖ϕ‖Lpν(0,∞) is the conventional norm given by
‖ϕ‖Lpν(0,∞)=(∫∞0|ϕ(x)|pσν(dx))1/p. |
● S∗(R) signifies the space of even functions on R that are infinitely differentiable and decrease rapidly, along with all their derivatives.
For ν≥−1/2, the Fourier-Bessel transform Fνϕ of ϕ∈L1ν(0,∞) is defined as:
Fνϕ(x):=∫∞0ϕ(t)Jν(tx)σν(dx),ν≥−1/2. | (2.4) |
This integral transform can be extended to establish an isometry of L2ν(0,∞). For any function ϕ belonging to L1ν(0,∞)∩L2ν(0,∞), the following relationships hold [24, Prop. 5.Ⅲ.2]
∫∞0|ϕ(x)|2σν(dx)=∫∞0|Fνϕ(t)|2σν(dt). | (2.5) |
Furthermore, its inverse is expressed as:
ϕ(x)=∫∞0Fνϕ(t)Jν(tx)σν(dt). | (2.6) |
Moving forward, our focus shifts to the exploration of the generalized translation operator linked to the Bessel operator. This operator is symbolized as τxν and operates on functions belonging to L1ν(0,∞) according to the following expression [25, §3.4.1]:
τxνϕ(y)={∫π0ϕ(√x2+y2+2xycosθ)sin2νθdθ,ifν>−1/2,12(ϕ(x+y)+ϕ(x−y)),ifν=−1/2. | (2.7) |
With the help of this translation operator, one defines the convolution of ϕ∈L1ν(0,∞) and ψ∈Lpν(0,∞) for p∈[1,∞) as the element f∗νg of Lpν(0,∞) given by
(ϕ∗νψ)(x):=∫∞0(τxνϕ)(y)ψ(y)σν(dy),ν≥−1/2. | (2.8) |
The following properties are obvious.
● Fν(τxνϕ)(t)=Jν(xt)Fνϕ(t),
● Fν(ϕ∗νψ)(x)=Fνϕ(x)Fνψ(x).
In this section, we consider the space-fractional Bessel diffusion equation [17,18]
{∂tu(x,t)+(−Bν)α/2u(x,t)=0,x≥0,t>0,u(x,0)=ϕ(x),u(∞,t)=0,t>0, | (2.9) |
where the parameters ν and α are restricted by the condition ν≥−12, 1≤α≤2, and the space-fractional Bessel operator (−Bν)α/2, which is defined pointwise by the principal value integral [17],
(−Bν)α/2ϕ(x)=cα,νlimε→0+∫∞εϕ(x)−τξνϕ(ξ)ξα+1dξ, | (2.10) |
here, the normalization constant cν,α is given by
cν,α=2α+νΓ(ν+α2+1)Γ(ν+1)|Γ(−α2)|. |
Moreover, the Fourier-Bessel transform of the fractional Bessel operator is given by [17]
Fν((−Bν)α/2ϕ)(ξ)=ξαFνϕ(ξ). | (2.11) |
Let us denote the Fourier-Bessel transform of a function u(x,t) with respect to x as ˆu(ξ,t), where ξ≥0. Applying the Fourier-Bessel transform to both sides of the equation in (1.3), we obtain:
{∂tˆu(ξ,t)=−ξαˆu(ξ,t),ˆu(ξ,t)=ˆϕ(ξ). |
Then
ˆu(ξ,t)=ˆϕ(ξ)e−ξαt. |
Therefore,
u(x,t)=(Gα,νt∗ϕ)(x), |
where
Gα,νt(x)=Gα,ν(x,t)=∫∞0e−ξαtJν(ξx)σν(dξ). | (2.12) |
Using the following scaling rules for the Fourier-Bessel transform:
∫∞0f(ax)Jν(λx)σν(dx)=1a2ν+2∫∞0f(x)Jν(λx/a)σν(dx),a>0, |
we obtain the following scaling property of the kernel Gα,ν(x,t)
Gα,ν(x,t)=t−2(ν+1)/αGα,ν(xt−1/α,1),t>0,x≥0. |
Consequently by introducing the similarity variable x/tα, we can write
Gα,ν(x,t)=t−2(ν+1)/αKα,ν(xt−1/α), |
where
Kα,ν(x)=∫∞0e−ξαJν(ξx)σν(dξ). | (2.13) |
Particular cases of the density Kα,ν are the following [26]:
● The density K2,ν(x), where ν≥−12, corresponds to the Gaussian density kernel:
K2,ν(x)=e−x242ν+1. | (2.14) |
● The density K1,ν, where ν≥−12, corresponds to the Poisson density:
K1,ν(x)=2ν+1Γ(ν+32)√π1(1+x2)ν+32. | (2.15) |
More generally, for 1<α<2, we have [17, Proposition 4.1]:
Kα,ν(x)=1α2ν∞∑n=0(−1)nn!Γ(2α(n+ν+1))Γ(ν+1+n)(x24)n. | (2.16) |
Definition 2.1. For ν≥−12 and 0<α<2, the generalized heat kernel is defined as:
Gα,ν(x,y,t)=∫∞0e−tξαJν(xξ)Jν(yξ)σν(dξ),x,y∈[0,∞). | (2.17) |
Lemma 2.1. The heat kernel Gα,ν(x,y,t) possesses the following properties:
i) Gα,ν(x,y,t)=τyνGα,ν(x,t).
ii) Gα,ν(x,y,t)=Gα,ν(y,x,t).
iii) Gα,ν(x,y,t)>0.
iv) ‖Gα,ν(x,y,t)‖L1ν(0,∞)=1.
v) Gα,ν(x,y,t)=t−2(ν+1)/αGα,ν(xt−1/α,yt−1/α,1).
vi) Fν(Gα,ν(⋅,y,t))(ξ)=e−tξαJν(yξ).
Proof. The proofs for properties i), ii), v), and vi) are straightforward. Property iii) follows from [18, Theorem 7] and the positivity of the generalized translation operator τyν. The proof for property iv) is derived by setting ξ=0 in the formula from property vi.
Definition 3.1. The generalized Weierstrass transform associated with the fractional Bessel operator, denoted as Wα,ν,t, is defined on L2(dσν) by the following expression:
(Wα,ν,tϕ)(y)=(Sα,νt∗νϕ)(y)=∫∞0Sα,ν(x,y,t)ϕ(x)σν(dx), |
where Ss,ν(x,y,t) is the generalized heat defined in (2.17).
Let s>ν+1. We define the space H(s)ν as follows [1]:
H(s)ν:={ϕ∈L2ν(0,∞):(1+ξ2)s/2Fν(ϕ)(ξ)∈L2ν(0,∞)}. | (3.1) |
This space is equipped with an inner product defined by:
⟨ϕ,ψ⟩H(s)ν=∫∞0(1+ξ2)sFν(ϕ)(ξ)Fν(ψ)(ξ)σν(dξ), |
and a norm:
‖ϕ‖H(s)ν=√⟨ϕ,ϕ⟩H(s)ν. |
The space H(s)ν features a reproducing kernel, which is defined by:
Ks(x,y)=∫∞0Jν(ξx)Jν(ξy)(1+ξ2)sσν(dξ),for (x,y)∈[0,∞)×[0,∞). | (3.2) |
Additionally, this space satisfies the following inclusions:
H(s)ν⊂L2ν(0,∞),Fν(H(s)ν)⊂L1ν(0,∞)∩L2ν(0,∞). |
For further details concerning the space H(s)ν, readers are referred to the paper [1].
Theorem 3.1. i) Let ϕ∈C0(R)∩L2ν(0,∞). For t>0 and x∈[0,∞), the function Wα,μ,tϕ(x) solves the following heat equation.
∂tu(x,t)=−(−Δν)γ/2u(x,t), |
with the initial condition
limt→0+Wα,ν,tϕ=ϕinL2ν(0,∞). |
ii) The integral transform Wα,ν,t, for t>0, is a one-to-one bounded linear operator from H(s)ν into L2ν(0,∞), and we have:
‖Wα,ν,tϕ‖L2ν(0,∞)≤‖ϕ‖H(s)ν,ϕ∈H(s)ν. |
Proof. The claim i) follows from [17, Theorem 4.5]. From Lemma 2.1, for all ϕ∈L2ν(0,∞), we have:
‖Wα,ν,tϕ‖L2ν(0,∞)=‖(Sα,νt∗νϕ)‖L2ν(0,∞)≤‖Sα,νt‖L1ν(0,∞)‖ϕ‖L2ν(0,∞)=‖ϕ‖L2ν(0,∞)=‖Fνϕ‖L2ν(0,∞)≤‖ϕ‖H(s)ν. |
This inequality shows that the transform Wα,ν,t is indeed bounded. To complete the proof of ii), it remains to show that this transform is one-to-one. Let ϕ∈H(s)ν such that Wα,ν,tϕ=0. Then
Fν(Wα,ν,tϕ)(ξ)=e−tξαFνϕ(ξ)=0, |
from the injectivity of the Fourier-Bessel transform, we get ϕ=0. This shows that Wα,ν,t is one-to-one.
We denote by H(s)ν,α,γ, the space H(s)ν equipped with the inner product
⟨ϕ|ψ⟩H(s)ν,α,γ=γ⟨ϕ|ψ⟩H(s)ν+⟨Wα,ν,tϕ|Wα,ν,tψ⟩L2ν(0,∞), |
and the norm
‖ϕ‖H(s)ν,α,γ=(γ‖ϕ‖2H(s)ν+‖Wνs,tϕ‖2L2ν(0,∞))1/2. |
Then, we have the following main result:
Theorem 3.2. Let ξ, t>0 and s>ν+1. Then the Hilbert space H(s)ν,α,γ admits the following reproducing kernel:
Kν,s,α,γ(x,y)=∫∞0Jν(xξ)Jν(yξ)γ(1+ξ2)s+e−2tξασν(dξ), |
that is
(i)For all x∈[0,∞), the function y↦Kν,s,α,γ(x,y) belongs to H(s)ν,α,γ.
(ii) For all ϕ∈H(s)ν,α,γ and any y∈[0,∞).
⟨ϕ,Kν,s,α,γ(⋅,y)⟩H(s)ν,α,γ=ϕ(y). |
Proof. For all x∈[0,∞), consider the function
ξ↦Jν(ξx)γ(1+ξ2)s+e−2tξα, |
which belongs to both L1ν(0,∞) and L2ν(0,∞). Then, by the Plancherel theorem for the Fourier-Bessel transform, the function
Kν,s,α,γ(x,y)=Fν(Jν(ξx)γ(1+ξ2)s+e−2tξα)(y), | (3.3) |
is well-defined. Following this,
ξ↦(1+ξ2)s/2Fν(Kν,s,α,γ(⋅,y))(ξ), |
is a member of L2ν(0,∞). This demonstrates that for all y≥0, the function Kν,s,α,γ(⋅,y) belongs to H(s)ν. This establishes part (ⅰ) of the theorem.
Let ϕ∈H(s)ν and y∈[0,∞). By Eq (3.3), we have
⟨ϕ,Kν,s,α,γ(⋅,y)⟩H(s)ν=∫∞0(1+ξ2)sJν(ξy)γ(1+ξ2)s+e−2tξαFνϕ(ξ)σν(dξ). | (3.4) |
From the relation
Wα,ν,tϕ=F−1ν(e−tξαFν(ϕ)), |
the action of Wα,ν,t on the kernel Kν,s,α,γ(⋅,y) is then:
Wα,ν,t(Kν,s,α,γ(⋅,y))=Sνs,t∗Kν,s,α,γ(⋅,y)=F−1ν(Fν(Sνs,t)⋅Fν(Kν,s,α,γ(⋅,y)))=F−1ν(e−tξαJν(ξy)γ(1+ξ2)s+e−2tξα). |
Therefore
⟨Wα,ν,tϕ,Wα,ν,tKν,s,α,γ(⋅,y)⟩L2ν(0,∞)=∫∞0e−2tξαJν(ξy)γ(1+ξ2)s+e−2tξασν(dξ). | (3.5) |
Combining Eqs (3.4) and (3.5), we get
⟨ϕ,Kν,s,α,γ(⋅,y)⟩H(s)ν,α,γ=γ∫∞0(1+ξ2)sJν(ξy)γ(1+ξ2)s+e−2tξαFνϕ(ξ)σν(dξ)+∫∞0e−2tξαJν(ξy)γ(1+ξ2)s+e−2tξαFνϕ(ξ)σν(dξ)=∫∞0Jν(ξy)Fνϕ(ξ)σν(dξ)=ϕ(y). |
This confirms the reproducing property (ⅱ).
We now consider the variational functional associated with the generalized Weierstrass integral transform Wα,ν,t, defined as
Jγ(ϕ)=12‖Wα,ν,tϕ−ψ‖2L2ν(0,∞)+γ2‖ϕ‖2H(s)ν,ϕ∈H(s)ν. | (4.1) |
For γ>0, the functional Jγ is strictly convex and Jγ(ϕ)≥γ2‖ϕ‖ν. Hence, Jγ has a unique minimizer, which can be characterized by the first-order condition
⟨J′γ(ϕ),φ⟩=0,for allφ∈H(s)ν, | (4.2) |
where J′γ(ϕ) is the Fréchet differential of Jγ. We denote by Rγ,ψ the regularized solution of the Eq (4.2), that is,
Rγ,ψ=minϕ∈HsνJγ(ϕ). | (4.3) |
The following theorem is our second main result.
Theorem 4.1. For ν≥−1/2, γ>0, and ψ∈L2ν(0,∞). Then there is a unique function Rγ,ψ∈H(s)ν, where the infimum of the functional Jγ, defined by
Jγ(ϕ)=12‖Wα,ν,tϕ−ψ‖2L2ν(0,∞)+γ2‖ϕ‖2H(s)ν,ϕ∈Hsν, | (4.4) |
is attained. Furthermore, the regularized function Rγ,ψ is given by
Rγ,ψ(x)=∫∞0Nν,s,α,γ(x,y)ψ(y)σν(dy), | (4.5) |
where
Nν,s,α,γ(x,y)=∫∞0e−tξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e−2tξασν(dξ). |
Proof. Observe that
Jγ(ϕ+εΔϕ)−Jγ(ϕ)=12{‖Wα,ν,t(ϕ+εΔϕ)−ψ‖2L2ν(0,∞)−‖Wα,ν,tϕ−ψ‖2L2ν(0,∞)}+γ2{‖ϕ+εΔϕ‖2H(s)ν−‖ϕ‖2H(s)ν}, |
where Δϕ denotes the increment.
Since,
(FνWα,ν,tϕ)(ξ)=e−tξα(Fνϕ)(ξ). | (4.6) |
Taking into account Eq (4.6) and using the Plancheral formula for the Fourier-Bessel transform to get
Jγ(ϕ+εΔϕ)−Jγ(ϕ)=12{‖e−tξαFν(ϕ+εΔϕ)(ξ)−Fνψ(ξ)‖2L2ν(0,∞)−‖e−tξαFνϕ(ξ)−Fνψ(ξ)‖2L2ν(0,∞)}+γ2{‖(ϕ+εΔϕ‖2Hs,γν,α−‖ϕ‖2H(s)ν}=ε{Re⟨e−tξαFνϕ(ξ)−Fνψ(ξ),e−tξαFνΔϕ(ξ)⟩L2ν(0,∞)+γRe⟨(1+ξ2)sFνϕ(ξ),FνΔϕ⟩L2ν(0,∞)}+ε22{‖e−tξαFνΔϕ(ξ)‖2L2ν(0,∞)+γ‖(1+ξ2)sFνΔϕ(ξ)‖2H(s)ν}. |
Hence, the Fréchet differential of Jγ can be written as
⟨J′γ(ϕ),Δϕ⟩L2ν(0,∞)=⟨e−2tξαFνϕ(ξ)−e−tξαFνψ(ξ)+γ(1+ξ2)sFνϕ,FνΔϕ⟩L2ν(0,∞). |
By the Perseval formula for the Fourier-Bessel transform, it follows that the regularized solution Rγ,ψ(ξ) is given by
e−2tξαFνRγ,ψ(ξ)−e−tξαFνψ(ξ)+γ(1+ξ2)sFνRγ,ψ(ξ)=0. |
Therefore
FνRγ,ψ(ξ)=e−tξαγ(1+ξ2)s+e−2tξαFνψ(ξ). |
It is easy to see that
ξ→e−tξαγ(1+ξ2)s+e−2tξαFνψ(ξ)∈L1ν(0,∞)∩L2ν(0,∞). |
By the inversion formula for the Fourier-Bessel transform, we have
Rγ,ψ(x)=F−1ν(e−tξαFνψ(ξ)γ(1+ξ2)s+e−2tξα)(x). |
We have
Rγ,ψ(x)=F−1ν(e−tξαFνψ(ξ)γ(1+ξ2)s+e−2tξα)(x)=∫∞0e−tξαFνψ(ξ)γ(1+ξ2)s+e−2tξαJν(xξ)σν(dξ)=∫∞0∫∞0e−tξαψ(y)γ(1+ξ2)s+e−2tξαJν(xξ)Jν(yξ)σν(dy)σν(dξ)=∫∞0(∫∞0e−tξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e−2tξασν(dξ))ψ(y)σν(dy)=∫∞0Nν,s,α,γ(x,y)ψ(y)σν(dy), |
where
Nν,s,α,γ(x,y)=∫∞0e−tξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e−2tξασν(dξ)=Fν(e−tξαJν(xξ)γ(1+ξ2)s+e−2tξα)(y). |
Theorem 4.2. Let s>ν+1. For all ψ1,ψ2∈L2ν(0,∞), the following inequality holds:
‖Rγ,ψ1−Rγ,ψ2‖H(s)ν≤14γ1/2‖ψ1−ψ2‖L2ν(0,∞). |
Proof. Consider any ψ1,ψ2∈L2ν(0,∞). The squared norm of the difference between the operators Rγ,ψ1 and Rγ,ψ2 in the space H(s)ν are given by:
‖Rγ,ψ1−Rγ,ψ2‖2H(s)ν=∫∞0(1+ξ2)s|Fν(Rγ,ψ1)(ξ)−Fν(Rγ,ψ2)(ξ)|2σν(dξ). |
Using the formula for the Fourier-Bessel transform of the extremal function Rγ,ψi:
Fν(Rγ,ψi)(ξ)=e−tξαγ(1+ξ2)s+e−2tξαFν(ψi)(ξ)for i=1,2, |
we can express the integral as:
‖Rγ,ψ1−Rγ,ψ2‖2H(s)ν=∫∞0(1+ξ2)se−2tξα(γ(1+ξ2)s+e−2tξα)2|Fν(ψ1)(ξ)−Fν(ψ2)(ξ)|2σν(dξ). |
By using the inequality
(1+ξ2)se−2tξα(γ(1+ξ2)s+e−2tξα)2≤14γ, |
we can further estimate:
‖Rγ,ψ1−Rγ,ψ2‖2H(s)ν≤14γ∫∞0|Fν(ψ1)(ξ)−Fν(ψ2)(ξ)|2σν(dξ)=14γ‖ψ1−ψ2‖2L2ν(0,∞). |
This completes the proof.
Proposition 4.1. Let s>ν+1, γ>0, and ψ∈L2ν(0,∞). We have the following estimate:
∫∞0|Rγ,ψ(ξ)|2σν(dξ)≤aν,αγ∫∞0eξα|ψ(ξ)|2σν(dξ). |
where
aν,α=Γ(2(ν+1)α)Γ(s−ν−1)α22ν+3Γ(s)Γ(ν+1). |
Proof. From (4.5) and applying the Cauchy-Schwarz inequality, we have:
|Rγ,ψ(ξ)|2≤(∫∞0|Nν,s,α,γ(x,y)ψ(y)|σν(dy))2≤∫∞0e−yα/2σν(dy)∫∞0eyα|Nν,s,α,γ(x,y)|2|ψ(y)|2σν(dy). |
Integrating over [0,∞) with respect to the measure σν(dx), we obtain:
‖Rγ,ψ(ξ)‖2L2ν(0,∞)≤(∫∞0|Nν,s,α,γ(x,y)ψ(y)|σν(dy))2≤∫∞0e−yα/2σν(dy)∫∞0eyα‖Nν,s,α,γ(x,y)‖2L2ν(0,∞)|ψ(y)|2σν(dy). |
However,
Nν,s,α,γ(x,y)=Fν(e−tξαJν(xξ)γ(1+ξ2)s+e−2tξα)(y), |
it follows that
‖Nν,s,α,γ(x,y)‖2L2ν(0,∞)=∫∞0e−2tξα|Jν(xξ)|2(γ(1+ξ2)s+e−2tξα)2σν(dξ)≤14γ∫∞01(1+ξ2)sσν(dξ). |
Therefore,
‖Rγ,ψ(ξ)‖2L2ν(0,∞)≤∫∞0e−yα/2σν(dy)14γ∫∞01(1+ξ2)sσν(dξ)∫∞0eyα|ψ(y)|2σν(dy). | (4.7) |
We complete the proof by using the relation (3.2) and the fact that:
∫∞0e−yασν(dy)=Γ(2(ν+1)α)α2νΓ(ν+1), |
and
∫∞0σν(dξ)(1+ξ2)s=Γ(s−ν−1)2ν+1Γ(s). |
Theorem 4.3. Let s>ν+1. For every ϕ∈H(s)ν and ψ=Wα,ν,t(ϕ), we have:
limγ→0+‖Rγ,ψ−ϕ‖H(s)ν=0. | (4.8) |
Moreover, the set {Rγ,ψ}γ>0 converges uniformly to ϕ as γ→0+.
Proof. Let ϕ∈H(s)ν and ψ=Wα,ν,t(ϕ). Utilizing the formula given in Eq (4.6), the Fourier-Bessel transform of the extremal function Rγ,ψ takes the form:
Fν(Rγ,ψ)(ξ)=e−tξαγ(1+ξ2)s+e−2tξαFν(ψ)(ξ)=e−2tξαγ(1+ξ2)s+e−2tξαFν(ϕ)(ξ). |
We can express the norm of the difference between Rγ,ψ and ϕ in H(s)ν as:
‖Rγ,ψ−ϕ‖2H(s)ν=∫∞0(1+ξ2)s|e−2tξαγ(1+ξ2)s+e−2tξα−1|2|Fν(ϕ)(ξ)|2σν(dξ)=∫∞0γ2(1+ξ2)3s(γ(1+ξ2)s+e−2tξα)2|Fν(ϕ)(ξ)|2σν(dξ). |
Using the dominated convergence theorem and observing that
γ2(1+ξ2)3s(γ(1+ξ2)s+e−2tξα)2|Fν(ϕ)(ξ)|2≤(1+ξ2)s|Fν(ϕ)(ξ)|2, |
and given that ϕ∈H(s)ν, we deduce that
limγ→0+‖Rγ,ψ−ϕ‖H(s)ν=0. |
The function Fν(ϕ) belongs to both L1ν(0,∞) and L2ν(0,∞). Applying the inversion formula for the Fourier-Bessel transform, we compute the deviation of the function ϕ under the operator Rγ,ψ as follows:
(Rγ,ψ−ϕ)(ξ)=−∫∞0γ(1+ξ2)sγ(1+ξ2)s+e−2tξαFν(ϕ)(ξ)σν(ξ)dξ. |
Thus, for all ξ∈[0,+∞[, the magnitude of the deviation is bounded by:
|(Rγ,ψ−ϕ)(ξ)|≤∫∞0γ(1+ξ2)sγ(1+ξ2)s+e−2tξα|Fν(ϕ)(ξ)|σν(ξ)dξ. |
Employing the dominated convergence theorem and noting that:
γ(1+ξ2)sγ(1+ξ2)s+e−2tξα|Fν(ϕ)(ξ)|≤|Fν(ϕ)(ξ)|, |
we deduce that the supremum over all ξ≥0 of the deviation approaches zero as γ tends towards zero
supξ∈[0,+∞[|(Rγ,ψ−ϕ)(ξ)|→0, as γ→0. |
This completes the proof of convergence in the H(s)ν norm and uniform convergence as γ→0+.
Example 4.1. As an illustrative example, consider the fractional heat equation on (0,∞)×(0,∞)
∂tu(x,t)=−(−∂xx)1/2u(x,t), |
with the initial condition
limt↘0‖u(⋅,t)−ϕ‖2,ν=0. |
To apply the Tikhonov regularization method to this fractional heat equation, we consider the integral operator W1,−1/2,t:H(1)−1/2→L2(0,∞) defined by
(W1,−1/2,tϕ)(y)=1√8π∫∞0ϕ(x+y)+ϕ(x−y)x2+t2xdx, |
where the Sobolev space H(1)−1/2 is realized by the reproducing kernel Hilbert space K1(x,y) given by
K1(x,y)=1√2π∫∞0cos(xu)cos(yu)u2+1du=√π32(exp(−|x−y|)+exp(−x−y)), |
where x,y≥0.
We now consider the following best approximation problem, that is, the Tikhonov functional. For any ψ∈L2(0,∞) and γ>0, we aim to solve
infϕ∈H(1)−1/2{γ2‖ϕ‖2H(1)−1/2+12‖W1,−1/2,tϕ−ψ‖2L2(0,∞)}. |
Then for the RKHS H(s)−1/2,1,γ consisting of all the members of H(1)−1/2 with the norm
‖ϕ‖H(1)−1/2=√γ‖ϕ‖2H(1)−1/2+‖W1,−1/2,tϕ‖2L2(0,∞), |
the reproducing kernel N−1/2,1,1,γ(x,y) can be calculated directly using the Fourier integrals as follows:
N−1/2,1,1,γ(x,y)=1√2π∫∞0exp(−tu)cos(xu)cos(yu)γ(1+u2)+exp(−2tu)du. |
Hence, the unique member of H(1)−1/2 with the minimum H(1)−1/2 norm—the function Rγ,ψ which attains the infimum—is given by
Rγ,ψ(x)=1√2π∫∞0N−1/2,1,1,γ(x,y)ψ(y)dy. | (4.9) |
For ϕ∈H(1)−1/2 and for ψ=W1,−1/2,tϕ, we have the formula
limγ→0Rγ,ψ(x)=ϕ(x), |
uniformly on (0,∞).
In this paper, we have explored the generalized Gauss-Weierstrass integral transform associated with the Bessel operator, emphasizing its application to space-fractional diffusion equations. This extension is significant because the Bessel operator coincides with the radial part of the Laplace operator, thereby broadening the scope of the classical transform.
We utilized the properties of the Fourier-Bessel transform and its connection with the ∗-convolution product to demonstrate that the transform Wα,ν,t is a one-to-one bounded linear operator from the Sobolev space Hsν into L2ν(0,∞). Given the ill-posed nature of the inverse problem, we applied Tikhonov regularization techniques to achieve stable reconstruction of functions. Our main theorem established the convergence of the regularized solution Rγ,ψ to the original function ϕ as the regularization parameter γ approaches zero.
The implications of our findings extend beyond theoretical interest, offering potential applications in solving inverse problems associated with fractional diffusion equations. Future research could further investigate the numerical implementation of the regularized inversion process and explore other types of fractional differential operators within this framework.
Overall, our work provides a robust foundation for the generalized Gauss-Weierstrass transform's utility in fractional calculus and opens avenues for further investigation into its applications in various mathematical contexts.
The author declares he/she has not used Artificial Intelligence (AI) tools in the creation of this article.
The author would like to extend their sincere appreciation to the Researchers Supporting Project number (RSPD2024R974), King Saud University, Riyadh, Saudi Arabia.
The author declares that there is no conflict of interest regarding the publication of this paper.
[1] |
A. Chavaillaz, A. Schwaninger, S. Michel, J. Sauer, Expertise, automation and trust in X-ray screening of cabin baggage, Front. Psychol., 10 (2019), 256. https://doi.org/10.3389/fpsyg.2019.00256 doi: 10.3389/fpsyg.2019.00256
![]() |
[2] | D. Turcsany, A. Mouton, T. P. Breckon, Improving feature-based object recognition for X-ray baggage security screening using primed visual words, in 2013 IEEE International Conference on Industrial Technology (ICIT), IEEE, (2013), 1140–1145. https://doi.org/10.1109/ICIT.2013.6505833 |
[3] | Z. Chen, Y. Zheng, B. R. Abidi, D. L. Page, M. A. Abidi, A combinational approach to the fusion, de-noising and enhancement of dual-energy X-ray luggage images, in 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, IEEE, (2005), 2. https://doi.org/10.1109/CVPR.2005.386 |
[4] |
B. R. Abidi, Y. Zheng, A. V. Gribok, M. A. Abidi, Improving weapon detection in single energy X-ray images through pseudo coloring, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 36 (2006), 784–796. https://doi.org/10.1109/TSMCC.2005.855523 doi: 10.1109/TSMCC.2005.855523
![]() |
[5] |
Q. Lu, R. W. Conners, Using image processing methods to improve the explosive detection accuracy, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 36 (2006), 750–760. https://doi.org/10.1109/TSMCC.2005.855532 doi: 10.1109/TSMCC.2005.855532
![]() |
[6] | T. W. Rogers, N. Jaccard, E. J. Morton, L. D. Griffin, Detection of cargo container loads from X-ray images, in 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), (2015), 1–6. https://doi.org/10.1049/cp.2015.1762 |
[7] | M. Kundegorski, S. Akçay, M. Devereux, A. Mouton, T. Breckon, On using feature descriptors as visual words for object detection within X-ray baggage security screening, in 7th International Conference on Imaging for Crime Detection and Prevention (ICDP), (2016), 1–6. https://doi.org/10.1049/ic.2016.0080 |
[8] | D. Mery, E. Svec, M. Arias, Object recognition in baggage inspection using adaptive sparse representations of X-ray images, in Image and Video Technology, Springer, 9431 (2016), 709–720. https://doi.org/10.1007/978-3-319-29451-3_56 |
[9] | T. Franzel, U. Schmidt, S. Roth, Object detection in multi-view X-ray images, in Pattern Recognition, Springer, 7476 (2012), 144–154. https://doi.org/10.1007/978-3-642-32717-9_15 |
[10] |
M. Bastan, Multi-view object detection in dual-energy X-ray images, Mach. Vision Appl., 26 (2015), 1045–1060. https://doi.org/10.1007/s00138-015-0706-x doi: 10.1007/s00138-015-0706-x
![]() |
[11] | G. Heitz, G. Chechik, Object separation in X-ray image sets, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2010), 2093–2100. https://doi.org/10.1109/CVPR.2010.5539887 |
[12] | O. K. Stamatis, N. Aouf, D. Nam, C. Belloni, Automatic X-ray image segmentation and clustering for threat detection, in Proceedings Volume 10432, Target and Background Signatures III, (2017), 104320O. https://doi.org/10.1117/12.2277190 |
[13] | D. Mery, Computer Vision Technology for X-ray Testing, Springer, 2015. https://doi.org/10.1007/978-3-319-20747-6 |
[14] |
D. Mery, V. Riffo, U. Zscherpel, G. Mondragón, I. Lillo, I. Zuccar, et al., GDXray: The database of X-ray images for nondestructive testing, J. Nondestr. Eval., 34 (2015), 42. https://doi.org/10.1007/s10921-015-0315-7 doi: 10.1007/s10921-015-0315-7
![]() |
[15] | T. W. Rogers, N. Jaccard, E. D. Protonotarios, J. Ollier, E. J. Morton, L. D. Griffin, Threat image projection (TIP) into X-ray images of cargo containers for training humans and machines, in 2016 IEEE International Carnahan Conference on Security Technology (ICCST), IEEE, (2016), 1–7. https://doi.org/10.1109/CCST.2016.7815717 |
[16] | C. Miao, L. Xie, F. Wan, C. Su, H. Liu, J. Jiao, et al., Sixray : A large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 2114–2123. https://doi.org/10.1109/CVPR.2019.00222 |
[17] | Y. Wei, R. Tao, Z. Wu, Y. Ma, L. Zhang, X. Liu, Occluded prohibited items detection: an X-ray security inspection benchmark and de-occlusion attention module, in Proceedings of the 28th ACM International Conference on Multimedia, ACM, (2020), 138–146. https://doi.org/10.1145/3394171.3413828 |
[18] |
J. Yang, Z. Zhao, H. Zhang, Y. Shi, Data augmentation for X-ray prohibited item images using generative adversarial networks, IEEE Access, 7 (2019), 28894–28902. https://doi.org/10.1109/ACCESS.2019.2902121 doi: 10.1109/ACCESS.2019.2902121
![]() |
[19] |
Y. Zhu, Y. Zhang, H. Zhang, J. Yang, Z. Zhao, Data augmentation of X-ray images in baggage inspection based on generative adversarial networks, IEEE Access, 8 (2020), 86536–86544. https://doi.org/10.1109/ACCESS.2020.2992861 doi: 10.1109/ACCESS.2020.2992861
![]() |
[20] |
J. Liu, T. H. Lin, A framework for the synthesis of X-ray security inspection images based on generative adversarial networks, IEEE Access, 11 (2023), 63751–63760. https://doi.org/10.1109/ACCESS.2023.3288087 doi: 10.1109/ACCESS.2023.3288087
![]() |
[21] | I. Goodfellow, NIPS 2016 Tutorial: Generative adversarial networks, preprint, arXiv: 1701.00160. |
[22] |
X. Wu, K. Xu, P. Hall, A survey of image synthesis and editing with generative adversarial networks, Tsinghua Sci. Technol., 22 (2017), 660–674. https://doi.org/10.23919/TST.2017.8195348 doi: 10.23919/TST.2017.8195348
![]() |
[23] |
Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan, Y. Zheng, Recent progress on generative adversarial networks (GANs): A survey, IEEE Access, 7 (2019), 36322–36333. https://doi.org/10.1109/ACCESS.2019.2905015 doi: 10.1109/ACCESS.2019.2905015
![]() |
[24] | A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, preprint, arXiv: 1511.06434. |
[25] | M. Mirza, S. Osindero, Conditional generative adversarial nets, preprint, arXiv: 1411.1784. |
[26] | A. Odena, C. Olah, J. Shlens, Conditional image synthesis with auxiliary classifier GANs, preprint, arXiv: 1610.09585. |
[27] | X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel, InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets, preprint, arXiv: 1606.03657. |
[28] | M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial networks, in International Conference on Machine Learning, PMLR, (2017), 214–223. |
[29] | I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. C. Courville, Improved training of wasserstein GANs, in Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates, Inc., (2017), 5769–5779. |
[30] | H. Petzka, A. Fischer, D. Lukovnicov, On the regularization of wasserstein GANs, preprint, arXiv: 1709.08894. |
[31] | P. Isola, J. Y. Zhu, T. Zhou, A. A. Efros, Image-to-image translation with conditional adversarial networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2017), 5967–5976. https://doi.org/10.1109/CVPR.2017.632 |
[32] | T. C. Wang, M. Y. Liu, J. Y. Zhu, A. Tao, J. Kautz, B. Catanzaro, High-resolution image synthesis and semantic manipulation with conditional GANs, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, (2018), 8798–8807. https://doi.org/10.1109/CVPR.2018.00917 |
[33] | J. Y. Zhu, T. Park, P. Isola, A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, (2018), 2242–2251. https://doi.org/10.1109/ICCV.2017.244 |
[34] | T. Kim, M. Cha, H. Kim, J. K. Lee, J. Kim, Learning to discover cross-domain relations with generative adversarial networks, in Proceedings 34th International Conference Machine Learning, PMLR, (2017), 1857–1865. |
[35] | Z. Yi, H. Zhang, P. Tan, M. Gong, DualGAN: Unsupervised dual learning for image-to-image translation, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, (2017), 2849–2857. |
[36] | Y. Choi, M. Choi, M. Kim, J. W. Ha, S. Kim, J. Choo, StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation, in 2018 Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 8789–8797. https://doi.org/10.1109/CVPR.2018.00916 |
[37] | B. Mildenhall, P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, R. Ng, NeRF: Representing scenes as neural radiance fields for view Synthesis, preprint, arXiv: 2003.08934. |
[38] | S. J. Garbin, M. Kowalski, M. Johnson, J. Shotton, J. Valentin, Fastnerf: High-fidelity neural rendering at 200fps, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 14346–14355. |
[39] | Z. Li, S. Niklaus, N. Snavely, N. Snavely, O. Wang, Neural scene flow fields for space-time view synthesis of dynamic scenes, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 6498–6508. |
[40] | A. Yu, V. Ye, M. Tancik, M. Tancik, A. Kanazawa, Pixelnerf: Neural radiance fields from one or few images, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 4578–4587. |
[41] | Q. Wang, Z. Wang, K. Genova, P. P. Srinivasan, H. Zhou, J. T. Barron, et al., IBRnet: Learning multi-view image-based rendering, in 2021 Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 4688–4697. https://doi.org/10.1109/CVPR46437.2021.00466 |
[42] | K. Schwarz, Y. Liao, M. Niemeyer, A. Geiger, GRAF: Generative radiance fields for 3D-aware image synthesis, preprint, arXiv: 2007.02442. |
[43] | M. Niemeyer, A. Geiger, GIRAFFE: Representing scenes as compositional generative neural feature fields, in 2021 Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 11448–11459. https://doi.org/10.1109/CVPR46437.2021.01129 |
[44] | K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint, arXiv: 1409.1556. |
[45] | Q. Xu, G. Huang, Y. Yuan, C. Guo, Y. Sun, F. Wu, et al., An empirical study on evaluation metrics of generative adversarial net, preprint, arXiv: 1806.07755. |
[46] | M. Bińkowski, D. J. Sutherland, M. Arbel, A. Gretton, Demystifying MMD GANs, preprint, arXiv: 1801.01401. |
[47] | Ultralytics, YOLOv8 Project, GitHub. Available from: https://github.com/ultralytics/ultralytics. |