Enhancing Border Learning for Better Image Denoising

Enhancing Border Learning for Better Image Denoising

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...

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Bibliographic Details
Main Authors: Xin Ge, Yu Zhu, Liping Qi, Yaoqi Hu, Jinqiu Sun, Yanning Zhang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Mathematics
Subjects:
image denoising
convolutional neural network
padding
border effect
autoencoder
patch-based method
Online Access:https://www.mdpi.com/2227-7390/13/7/1119
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