Our goal is to obtain a noise-free, high resolution (HR) image, from an observed, noisy, low resolution (LR) image. The conventional approach of preprocessing the image with a denoising algorithm, followed by applying a super-resolution (SR) algorithm, has an important limitation: Along with noise, some high frequency content of the image (particularly textural detail) is invariably lost during the denoising step. This `denoising loss’ restricts the performance of the subsequent SR step, wherein the challenge is to synthesize such textural details. In this work, we show that high frequency content in the noisy image (which is ordinarily removed by denoising algorithms) can be effectively used to obtain the missing textural details in the HR domain. We show that part-recovery and part-synthesis of textures through our algorithm yields HR images that are visually more pleasing than those obtained using the conventional processing pipeline.
- A. Singh, F. Porikli, N. Ahuja, Super-Resolving Noisy Images, Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.