Image denoising with a convolution neural network using Gaussian filtered residuals

Deep learning using a convolutional neural network has become a state-of-art technique in image processing. In recent scenarios, image denoising using a residual image in deep learning has been popular. However, one aspect missing in these methods is that the residual image has all the noise and very small structured details of the input image. Therefore, we have developed a Gaussian filter residual convolutional neural network architecture for color image denoising. Gaussian residual learning was used to boost the denoising performance. The architecture is designed to remove additive white Gaussian noise, which is one of the most basic types of noise that affects an image when captured. The network with Gaussian residual learning removes the clean image using the features learned from the hidden layer. The peak signal-to-noise ratio and structural similarity index measure achieved by our method reveals that the presented approach is better at denoising images with Gaussian noise than a convolutional neural network. © 2021 Institute of Electronics and Information Engineers. All rights reserved.