Medical image denoising is particularly important in brain image processing. Noise in acquisition and transmission degrades image quality and affects the reliability of diagnosis and research. Due to the complexity of the brain's structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. This paper proposes a multi-convolutional neural network based on feature distillation learning and dense residual attention to enhance the quality of brain images and improve denoising performance. The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). Before feeding the brain images into the denoising network model, they are preprocessed using a modified watershed algorithm based on a combination of a morphological gradient, Sobel's operator, and Canny's operator. The GSN is used to extract global features and increase the sensory field, and the DRAN efficiently extracts key features by combining improved channel attention and spatial attention mechanisms. The FDN extracts useful features through two feature distillation blocks, suppresses redundant information, and reduces computational complexity. The FPB performs feature fusion. Experimental results on brain image datasets and ground-based open datasets show that the proposed model outperforms existing methods in several metrics, and helps to improve the accuracy of brain disease diagnosis and treatment.
Citation: Huimin Qu, Haiyan Xie, Qianying Wang. Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention[J]. Electronic Research Archive, 2025, 33(3): 1231-1266. doi: 10.3934/era.2025055
Medical image denoising is particularly important in brain image processing. Noise in acquisition and transmission degrades image quality and affects the reliability of diagnosis and research. Due to the complexity of the brain's structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. This paper proposes a multi-convolutional neural network based on feature distillation learning and dense residual attention to enhance the quality of brain images and improve denoising performance. The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). Before feeding the brain images into the denoising network model, they are preprocessed using a modified watershed algorithm based on a combination of a morphological gradient, Sobel's operator, and Canny's operator. The GSN is used to extract global features and increase the sensory field, and the DRAN efficiently extracts key features by combining improved channel attention and spatial attention mechanisms. The FDN extracts useful features through two feature distillation blocks, suppresses redundant information, and reduces computational complexity. The FPB performs feature fusion. Experimental results on brain image datasets and ground-based open datasets show that the proposed model outperforms existing methods in several metrics, and helps to improve the accuracy of brain disease diagnosis and treatment.
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Huimin Qu, Haiyan Xie, Qianying Wang. Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention[J]. Electronic Research Archive, 2025, 33(3): 1231-1266. doi: 10.3934/era.2025055
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