Deep Neural Network Convolution for Natural Image Denoising
Amin Zarshenas, Kenji Suzuki · 2018
Deep learning has recently proven extremely successful in many low-level image-processing tasks including natural image denoising. However, with regards to designing deep models for practical image processing, there are numerous essential viewpoints that one ought to consider. In this work, we first aimed to reply to probably the most critical design questions through theoretical analysis and extensive experiments: How deep and wide a deep denoiser should and can be? Does denoising performance get improved by using residual learning? Can and should we switch from the region-based to image-based models? And second, based on our analysis, we designed a deep neural network for natural image denoising which was hundred-layer deep, exploited both internal and external residual learning, and was trained in an image-based fashion. Our deep denoiser achieved the state-of-the-art results quantitatively and qualitatively on multiple datasets including one with more than 10,000 images.