Improvement Of Image Denoising Algorithms By Preserving The Edges

Image restoration is one of the well-studied problems in low-level image processing tasks. Recently, deep learning based image restoration techniques have shown promising results and outperform most of the state of the art image denoising algorithms. Most of the deep learning based methods use mean square error as a loss function to obtain the denoised output. This work focuses on further improving the existing deep learning based image denoising techniques by preserving edges using Canny edge based loss function, and hence improving peak signal to noise ratio (PSNR) and structural similarity (SSIM) of the images while restoring the visual quality.