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
Published in:	Mathematics (Basel) Vol. 13; no. 7; p. 1119
Main Authors:	Ge, Xin, Zhu, Yu, Qi, Liping, Hu, Yaoqi, Sun, Jinqiu, Zhang, Yanning
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2025
Subjects:	Accuracy Algorithms Analysis Artificial neural networks autoencoder border effect Borders convolutional neural network Deep learning Design image denoising Machine learning Neural networks Noise reduction padding patch-based method United States New Jersey
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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