Enhancing Border Learning for Better Image Denoising

Xin Ge, Yu Zhu (), Liping Qi (), Yaoqi Hu, Jinqiu Sun and Yanning Zhang
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Xin Ge: School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Yu Zhu: School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Liping Qi: College of Science and Technology, Hebei Agricultural University, Cangzhou 061100, China
Yaoqi Hu: School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Jinqiu Sun: School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
Yanning Zhang: School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

Mathematics, 2025, vol. 13, issue 7, 1-29

Abstract: Deep neural networks for image denoising typically follow an encoder–decoder model, with convolutional (Conv) layers as essential components. Conv layers apply zero padding at the borders of input data to maintain consistent output dimensions. However, zero padding introduces ring-like artifacts at the borders of output images, referred to as border effects, which negatively impact the network’s ability to learn effective features. In traditional methods, these border effects, associated with convolutional/deconvolutional operations, have been mitigated using patch-based techniques. Inspired by this observation, patch-wise denoising algorithms were explored to derive a CNN architecture that avoids border effects. Specifically, we extend the patch-wise autoencoder to learn image mappings through patch extraction and patch-averaging operations, demonstrating that the patch-wise autoencoder is equivalent to a specific convolutional neural network (CNN) architecture, resulting in a novel residual block. This new residual block includes a mask that enhances the CNN’s ability to learn border features and eliminates border artifacts, referred to as the Border-Enhanced Residual Block (BERBlock). By stacking BERBlocks, we constructed a U-Net denoiser (BERUNet). Experiments on public datasets demonstrate that the proposed BERUNet achieves outstanding performance. The proposed network architecture is built on rigorous mathematical derivations, making its working mechanism highly interpretable. The code and all pretrained models are publicly available.

Keywords: image denoising; convolutional neural network; padding; border effect; autoencoder; patch-based method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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