Multi scale pixel attention and feature extraction based neural network for image denoising

In this paper, we propose a blind Gaussian denoising network that utilize the features of the input image and its negative for generating denoised output of the same. The proposed network is a dual path model which employs a multi-scale pixel attention (MSPA) block on one path and a multi-scale feature extraction (MSFE) block on another. The concept of using the features of a negative image (that it highlights the low contrast region) in blind Gaussian denoising network is, to the best of our knowledge, a first such attempt. The proposed MSPA and MSFE blocks are designed to focus on the features of the image at multiple scales. The MSPA block focuses on the important features of the negative of the input image whereas the MSFE block focuses on extracting features of the input noisy image. The features of both the images are then combined and a final residual noise is obtained, subtracting which from the input noisy image produces the final denoised result. The proposed network is lightweight and fast, due to the low number of convolutional layers involved, and produces superior results (both quantitatively and qualitatively) when compared with various traditional and learning based blind Gaussian denoising techniques. The code of this paper can be downloaded from https://github.com/RTSIR/NIFBGDNet.