Blind Gaussian Deep Denoiser Network using Multi-Scale Pixel Attention

Many deep learning networks focus on the task of Gaussian denoising by processing images on a fixed scale or multiple scales using convolution and deconvolution. In certain cases, excessive scaling applied in the network results in the loss of image details. Sometimes, the usage of deeper convolutional networks results in the loss of network gradient. In this paper, to overcome both the problems, we propose a multi-scale pixel attention-based blind Gaussian denoiser network that utilizes a combination of important features at five different scales. The proposed network performs blind Gaussian denoising in the sense that it does not need any prior information about noise. It comprises a central multi-scale pixel attention block together with dilated convolutional layers and skip connections that help in utilizing the full receptive field of the first convolutional layer to the last convolutional layer and is based on residual architecture for propagating high-level information easily in the network. We have provided the code of the proposed technique at https://github.com/RTSIR/MSPABDN.