Mixed Gaussian-impulse noise reduction from images using convolutional neural network
作者：
Highlights：
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• A novel CNN-based method for reducing mixed Gaussian-impulse noise from images.
• New mapping from noisy to noise-free images using a 4-stage CNN architecture.
• Adoption of transfer learning for faster training of proposed CNN model.
• Experiments on challenging datasets with diverse settings of noise parameters.
• Results show that proposed method is better than existing or similar methods.
摘要
•A novel CNN-based method for reducing mixed Gaussian-impulse noise from images.•New mapping from noisy to noise-free images using a 4-stage CNN architecture.•Adoption of transfer learning for faster training of proposed CNN model.•Experiments on challenging datasets with diverse settings of noise parameters.•Results show that proposed method is better than existing or similar methods.
论文关键词：Convolutional neural network,Deep learning,Image denoising,Reduction of mixed-noise
论文评审过程：Received 25 January 2018, Revised 25 May 2018, Accepted 26 June 2018, Available online 3 July 2018, Version of Record 11 July 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.06.016
