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Related Experiment Video

Updated: Dec 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Image denoising using deep CNN with batch renormalization.

Chunwei Tian¹, Yong Xu², Wangmeng Zuo³

¹Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, Guangdong, China.

Neural Networks : the Official Journal of the International Neural Network Society

|October 20, 2019

Summary

This summary is machine-generated.

A new Batch-Renormalization Denoising Network (BRDNet) effectively denoises images by increasing network width and utilizing residual learning. This deep learning approach overcomes challenges in training deeper convolutional neural networks (CNNs) for superior image denoising performance.

Keywords:

Batch renormalization CNN Dilated convolution Image denoising Residual learning

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Deep convolutional neural networks (CNNs) show promise in image denoising.
Training deeper CNNs for denoising is challenging due to difficulties and performance saturation.

Purpose of the Study:

Introduce a novel Batch-Renormalization Denoising Network (BRDNet).
Address limitations of existing deep CNNs in image denoising tasks.

Main Methods:

Combined two networks to increase network width and feature extraction.
Integrated batch renormalization to mitigate internal covariate shift and small mini-batch issues.
Employed residual learning and dilated convolutions to enhance training and information extraction.

Main Results:

BRDNet demonstrates superior performance compared to state-of-the-art image denoising methods.
The network architecture effectively handles challenges in training deep CNNs.

Conclusions:

BRDNet offers an effective solution for image denoising.
The proposed architecture advances the capabilities of deep learning in image processing.