Beyond Gaussian Denoising: Deep CNN Residual Learning for Image Denoising
Shikha Sain
Abstract
The training of discriminative models for image denoising has recently attracted a lot of interest due to its efficient denoising performance. In this article, we look at the process used to build a direct denoising convolutional neural network (DnCNN). DnCNN has made significant advancements in picture denoising by integrating advancements in extremely deep structures, learning algorithms, and regularisation approaches. Residual learning and packet normalisation are used specifically to speed up learning and improve denoising performance. Blind Gaussian noise may be reduced using our DnCNN approach. This method does not employ additive white Gaussian noise (AWGN), in contrast to existing discriminative noise reduction strategies, which frequently train specific algorithms on AWGN at a certain noise level. In order to implicitly exclude potentially clean images from the buried layer, DnCNN uses a residual learning technique. Due to this property, we were able to perform a variety of common picture denoising tasks, including Gaussian denoising, single image super resolution, and JPEG image denoising. Our thorough testing demonstrates that the DnCNN model can be effectively implemented utilising GPU computation and executes well in a variety of standard picture denoising applications. Gaussian noise reduction and extremely unusual picture resolution. Our thorough testing demonstrates that the DnCNN model can be implemented effectively utilising GPU computation and executes well in a variety of standard picture denoising applications. Gaussian noise reduction and extremely unusual picture resolution. Our thorough testing demonstrates that the DnCNN model can be implemented effectively utilising GPU computation and executes well in a variety of standard picture denoising applications.
keywords: Batch normalization, residual learning, convolutional neural networks, image denoising
