Single Image Super Resolution Using Convolutional Neural Networks for Noisy Images
Tae Bok Lee, Yong Seok Heo · 2020
In this paper, we address a problem of image super resolution to obtain a noise-free and high resolution image from a noisy and low resolution image. In recent years, deep learning-based approaches have been achieved a lot of progress to the image restoration problems. However, it is still not trivial to generate a high quality image when the input image is both noisy and low-resolution, because it is difficult to disambiguate the fine texture components from noise components for the input image. In this case, conventional super-resolution algorithms usually amplify the noise along with the details. To deal with this problem, we propose a super-resolution network that is robust to noisy images by constructing multi-modules in parallel architecture. The experimental results show that our proposed network restores a noise-free and rich-texture image from the low-resolution and noisy input image, while other methods fail.