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Research article Special Issues

The 3D-aware image synthesis of prohibited items in the X-ray security inspection by stylized generative radiance fields

  • Received: 11 December 2023 Revised: 24 January 2024 Accepted: 30 January 2024 Published: 29 February 2024
  • 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

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  • 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)0ex2+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,ν12. (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,x0,t>0,u(x,0)=ϕ(x),t>0, (1.3)

    where the parameters ν and α are restricted by the condition ν12, 1α2, and the space-fractional Bessel operator (Bν)α/2, which is defined pointwise by the principal value integral [17],

    (Bν)α/2ϕ(x)=cα,νlimε0+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=n21, 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=12i, 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 W1α,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 ψL2ν(0,), we search for a minimizer of a functional given by

    Jγ(ϕ)=12Wα,ν,tϕψ2L2ν(0,)+γ2ϕ2Hsν,ϕHsν,

    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 ϕHsν and ψ=Wα,ν,t(ϕ), we have:

    limγ0+Rγ,ψϕH(s)ν=0.

    Moreover, the set {Rγ,ψ}γ>0 converges uniformly to ϕ as γ0+.

    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 xJν(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:

    J1/2(x)=cosx,J1/2(x)=sinxx.

    We introduce the following notation:

    Lpν(0,) (1p) represents the Lebesgue space associated with the measure

    σν(dx)=x2ν+12νΓ(ν+1)dx. (2.3)

    The norm ϕLpν(0,) is the conventional norm given by

    ϕLpν(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 ν1/2, the Fourier-Bessel transform Fνϕ of ϕL1ν(0,) is defined as:

    Fνϕ(x):=0ϕ(t)Jν(tx)σν(dx),ν1/2. (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(xy)),ifν=1/2. (2.7)

    With the help of this translation operator, one defines the convolution of ϕL1ν(0,) and ψLpν(0,) for p[1,) as the element fνg of Lpν(0,) given by

    (ϕνψ)(x):=0(τxνϕ)(y)ψ(y)σν(dy),ν1/2. (2.8)

    The following properties are obvious.

    Fν(τxνϕ)(t)=Jν(xt)Fνϕ(t),

    Fν(ϕνψ)(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,x0,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 ν12, 1α2, and the space-fractional Bessel operator (Bν)α/2, which is defined pointwise by the principal value integral [17],

    (Bν)α/2ϕ(x)=cα,νlimε0+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 ξ0. 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)eξαt.

    Therefore,

    u(x,t)=(Gα,νtϕ)(x),

    where

    Gα,νt(x)=Gα,ν(x,t)=0eξαtJν(ξx)σν(dξ). (2.12)

    Using the following scaling rules for the Fourier-Bessel transform:

    0f(ax)Jν(λx)σν(dx)=1a2ν+20f(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)=t2(ν+1)/αGα,ν(xt1/α,1),t>0,x0.

    Consequently by introducing the similarity variable x/tα, we can write

    Gα,ν(x,t)=t2(ν+1)/αKα,ν(xt1/α),

    where

    Kα,ν(x)=0eξαJν(ξx)σν(dξ). (2.13)

    Particular cases of the density Kα,ν are the following [26]:

    ● The density K2,ν(x), where ν12, corresponds to the Gaussian density kernel:

    K2,ν(x)=ex242ν+1. (2.14)

    ● The density K1,ν, where ν12, 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)=1α2νn=0(1)nn!Γ(2α(n+ν+1))Γ(ν+1+n)(x24)n. (2.16)

    Definition 2.1. For ν12 and 0<α<2, the generalized heat kernel is defined as:

    Gα,ν(x,y,t)=0etξαJν(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)=t2(ν+1)/αGα,ν(xt1/α,yt1/α,1).

    vi) Fν(Gα,ν(,y,t))(ξ)=etξαJν(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,νtνϕ)(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:={ϕL2ν(0,):(1+ξ2)s/2Fν(ϕ)(ξ)L2ν(0,)}. (3.1)

    This space is equipped with an inner product defined by:

    ϕ,ψH(s)ν=0(1+ξ2)sFν(ϕ)(ξ)Fν(ψ)(ξ)σν(dξ),

    and a norm:

    ϕH(s)ν=ϕ,ϕH(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)νL2ν(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 ϕC0(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

    limt0+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,tϕL2ν(0,)ϕH(s)ν,ϕH(s)ν.

    Proof. The claim i) follows from [17, Theorem 4.5]. From Lemma 2.1, for all ϕL2ν(0,), we have:

    Wα,ν,tϕL2ν(0,)=(Sα,νtνϕ)L2ν(0,)Sα,νtL1ν(0,)ϕL2ν(0,)=ϕL2ν(0,)=FνϕL2ν(0,)ϕH(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 ϕH(s)ν such that Wα,ν,tϕ=0. Then

    Fν(Wα,ν,tϕ)(ξ)=etξαFνϕ(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|ψH(s)ν,α,γ=γϕ|ψH(s)ν+Wα,ν,tϕ|Wα,ν,tψL2ν(0,),

    and the norm

    ϕH(s)ν,α,γ=(γϕ2H(s)ν+Wνs,tϕ2L2ν(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+e2tξασν(dξ),

    that is

    (i)For all x[0,), the function yKν,s,α,γ(x,y) belongs to H(s)ν,α,γ.

    (ii) For all ϕH(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

    ξJν(ξx)γ(1+ξ2)s+e2tξα,

    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+e2tξα)(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 y0, the function Kν,s,α,γ(,y) belongs to H(s)ν. This establishes part (ⅰ) of the theorem.

    Let ϕH(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+e2tξαFνϕ(ξ)σν(dξ). (3.4)

    From the relation

    Wα,ν,tϕ=F1ν(etξαFν(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,tKν,s,α,γ(,y)=F1ν(Fν(Sνs,t)Fν(Kν,s,α,γ(,y)))=F1ν(etξαJν(ξy)γ(1+ξ2)s+e2tξα).

    Therefore

    Wα,ν,tϕ,Wα,ν,tKν,s,α,γ(,y)L2ν(0,)=0e2tξαJν(ξy)γ(1+ξ2)s+e2tξασν(dξ). (3.5)

    Combining Eqs (3.4) and (3.5), we get

    ϕ,Kν,s,α,γ(,y)H(s)ν,α,γ=γ0(1+ξ2)sJν(ξy)γ(1+ξ2)s+e2tξαFνϕ(ξ)σν(dξ)+0e2tξαJν(ξy)γ(1+ξ2)s+e2tξαFνϕ(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)=12Wα,ν,tϕψ2L2ν(0,)+γ2ϕ2H(s)ν,ϕH(s)ν. (4.1)

    For γ>0, the functional Jγ is strictly convex and Jγ(ϕ)γ2ϕν. Hence, Jγ has a unique minimizer, which can be characterized by the first-order condition

    Jγ(ϕ),φ=0,for allφH(s)ν, (4.2)

    where Jγ(ϕ) is the Fréchet differential of Jγ. We denote by Rγ,ψ the regularized solution of the Eq (4.2), that is,

    Rγ,ψ=minϕHsνJγ(ϕ). (4.3)

    The following theorem is our second main result.

    Theorem 4.1. For ν1/2, γ>0, and ψL2ν(0,). Then there is a unique function Rγ,ψH(s)ν, where the infimum of the functional Jγ, defined by

    Jγ(ϕ)=12Wα,ν,tϕψ2L2ν(0,)+γ2ϕ2H(s)ν,ϕHsν, (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)=0etξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e2tξασν(dξ).

    Proof. Observe that

    Jγ(ϕ+εΔϕ)Jγ(ϕ)=12{Wα,ν,t(ϕ+εΔϕ)ψ2L2ν(0,)Wα,ν,tϕψ2L2ν(0,)}+γ2{ϕ+εΔϕ2H(s)νϕ2H(s)ν},

    where Δϕ denotes the increment.

    Since,

    (FνWα,ν,tϕ)(ξ)=etξα(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{etξαFν(ϕ+εΔϕ)(ξ)Fνψ(ξ)2L2ν(0,)etξαFνϕ(ξ)Fνψ(ξ)2L2ν(0,)}+γ2{(ϕ+εΔϕ2Hs,γν,αϕ2H(s)ν}=ε{ReetξαFνϕ(ξ)Fνψ(ξ),etξαFνΔϕ(ξ)L2ν(0,)+γRe(1+ξ2)sFνϕ(ξ),FνΔϕL2ν(0,)}+ε22{etξαFνΔϕ(ξ)2L2ν(0,)+γ(1+ξ2)sFνΔϕ(ξ)2H(s)ν}.

    Hence, the Fréchet differential of Jγ can be written as

    Jγ(ϕ),ΔϕL2ν(0,)=e2tξαFνϕ(ξ)etξαFνψ(ξ)+γ(1+ξ2)sFνϕ,FνΔϕL2ν(0,).

    By the Perseval formula for the Fourier-Bessel transform, it follows that the regularized solution Rγ,ψ(ξ) is given by

    e2tξαFνRγ,ψ(ξ)etξαFνψ(ξ)+γ(1+ξ2)sFνRγ,ψ(ξ)=0.

    Therefore

    FνRγ,ψ(ξ)=etξαγ(1+ξ2)s+e2tξαFνψ(ξ).

    It is easy to see that

    ξetξαγ(1+ξ2)s+e2tξαFνψ(ξ)L1ν(0,)L2ν(0,).

    By the inversion formula for the Fourier-Bessel transform, we have

    Rγ,ψ(x)=F1ν(etξαFνψ(ξ)γ(1+ξ2)s+e2tξα)(x).

    We have

    Rγ,ψ(x)=F1ν(etξαFνψ(ξ)γ(1+ξ2)s+e2tξα)(x)=0etξαFνψ(ξ)γ(1+ξ2)s+e2tξαJν(xξ)σν(dξ)=00etξαψ(y)γ(1+ξ2)s+e2tξαJν(xξ)Jν(yξ)σν(dy)σν(dξ)=0(0etξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e2tξασν(dξ))ψ(y)σν(dy)=0Nν,s,α,γ(x,y)ψ(y)σν(dy),

    where

    Nν,s,α,γ(x,y)=0etξαJν(xξ)Jν(yξ)γ(1+ξ2)s+e2tξασν(dξ)=Fν(etξαJν(xξ)γ(1+ξ2)s+e2tξα)(y).

    In the following theorem, we will provide an error estimate for the inversion formula.

    Theorem 4.2. Let s>ν+1. For all ψ1,ψ2L2ν(0,), the following inequality holds:

    Rγ,ψ1Rγ,ψ2H(s)ν14γ1/2ψ1ψ2L2ν(0,).

    Proof. Consider any ψ1,ψ2L2ν(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,ψ1Rγ,ψ22H(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)=etξαγ(1+ξ2)s+e2tξαFν(ψi)(ξ)for i=1,2,

    we can express the integral as:

    Rγ,ψ1Rγ,ψ22H(s)ν=0(1+ξ2)se2tξα(γ(1+ξ2)s+e2tξα)2|Fν(ψ1)(ξ)Fν(ψ2)(ξ)|2σν(dξ).

    By using the inequality

    (1+ξ2)se2tξα(γ(1+ξ2)s+e2tξα)214γ,

    we can further estimate:

    Rγ,ψ1Rγ,ψ22H(s)ν14γ0|Fν(ψ1)(ξ)Fν(ψ2)(ξ)|2σν(dξ)=14γψ1ψ22L2ν(0,).

    This completes the proof.

    Proposition 4.1. Let s>ν+1, γ>0, and ψL2ν(0,). We have the following estimate:

    0|Rγ,ψ(ξ)|2σν(dξ)aν,αγ0eξα|ψ(ξ)|2σν(dξ).

    where

    aν,α=Γ(2(ν+1)α)Γ(sν1)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))20eyα/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))20eyα/2σν(dy)0eyαNν,s,α,γ(x,y)2L2ν(0,)|ψ(y)|2σν(dy).

    However,

    Nν,s,α,γ(x,y)=Fν(etξαJν(xξ)γ(1+ξ2)s+e2tξα)(y),

    it follows that

    Nν,s,α,γ(x,y)2L2ν(0,)=0e2tξα|Jν(xξ)|2(γ(1+ξ2)s+e2tξα)2σν(dξ)14γ01(1+ξ2)sσν(dξ).

    Therefore,

    Rγ,ψ(ξ)2L2ν(0,)0eyα/2σν(dy)14γ01(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:

    0eyασν(dy)=Γ(2(ν+1)α)α2νΓ(ν+1),

    and

    0σν(dξ)(1+ξ2)s=Γ(sν1)2ν+1Γ(s).

    Theorem 4.3. Let s>ν+1. For every ϕH(s)ν and ψ=Wα,ν,t(ϕ), we have:

    limγ0+Rγ,ψϕH(s)ν=0. (4.8)

    Moreover, the set {Rγ,ψ}γ>0 converges uniformly to ϕ as γ0+.

    Proof. Let ϕH(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)=etξαγ(1+ξ2)s+e2tξαFν(ψ)(ξ)=e2tξαγ(1+ξ2)s+e2tξαFν(ϕ)(ξ).

    We can express the norm of the difference between Rγ,ψ and ϕ in H(s)ν as:

    Rγ,ψϕ2H(s)ν=0(1+ξ2)s|e2tξαγ(1+ξ2)s+e2tξα1|2|Fν(ϕ)(ξ)|2σν(dξ)=0γ2(1+ξ2)3s(γ(1+ξ2)s+e2tξα)2|Fν(ϕ)(ξ)|2σν(dξ).

    Using the dominated convergence theorem and observing that

    γ2(1+ξ2)3s(γ(1+ξ2)s+e2tξα)2|Fν(ϕ)(ξ)|2(1+ξ2)s|Fν(ϕ)(ξ)|2,

    and given that ϕH(s)ν, we deduce that

    limγ0+Rγ,ψϕH(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,ψϕ)(ξ)=0γ(1+ξ2)sγ(1+ξ2)s+e2tξαFν(ϕ)(ξ)σν(ξ)dξ.

    Thus, for all ξ[0,+[, the magnitude of the deviation is bounded by:

    |(Rγ,ψϕ)(ξ)|0γ(1+ξ2)sγ(1+ξ2)s+e2tξα|Fν(ϕ)(ξ)|σν(ξ)dξ.

    Employing the dominated convergence theorem and noting that:

    γ(1+ξ2)sγ(1+ξ2)s+e2tξα|Fν(ϕ)(ξ)||Fν(ϕ)(ξ)|,

    we deduce that the supremum over all ξ0 of the deviation approaches zero as γ tends towards zero

    supξ[0,+[|(Rγ,ψϕ)(ξ)|0, as γ0.

    This completes the proof of convergence in the H(s)ν norm and uniform convergence as γ0+.

    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

    limt0u(,t)ϕ2,ν=0.

    To apply the Tikhonov regularization method to this fractional heat equation, we consider the integral operator W1,1/2,t:H(1)1/2L2(0,) defined by

    (W1,1/2,tϕ)(y)=18π0ϕ(x+y)+ϕ(xy)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)=12π0cos(xu)cos(yu)u2+1du=π32(exp(|xy|)+exp(xy)),

    where x,y0.

    We now consider the following best approximation problem, that is, the Tikhonov functional. For any ψL2(0,) and γ>0, we aim to solve

    infϕH(1)1/2{γ2ϕ2H(1)1/2+12W1,1/2,tϕψ2L2(0,)}.

    Then for the RKHS H(s)1/2,1,γ consisting of all the members of H(1)1/2 with the norm

    ϕH(1)1/2=γϕ2H(1)1/2+W1,1/2,tϕ2L2(0,),

    the reproducing kernel N1/2,1,1,γ(x,y) can be calculated directly using the Fourier integrals as follows:

    N1/2,1,1,γ(x,y)=12π0exp(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)=12π0N1/2,1,1,γ(x,y)ψ(y)dy. (4.9)

    For ϕH(1)1/2 and for ψ=W1,1/2,tϕ, we have the formula

    limγ0Rγ,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.



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