Maximizing Nonlocal Self‐Similarity Prior for Single Image Super‐Resolution

Abstract

Prior knowledge plays an important role in the process of image super-resolution reconstruction, which can constrain the solution space efficiently. In this paper, we utilized the fact that clear image exhibits stronger self-similarity property than other degradated version to present a new prior called maximizing nonlocal self-similarity for single image super-resolution. For describing the prior with mathematical language, a joint Gaussian mixture model was trained with LR and HR patch pairs extracted from the input LR image and its lower scale, and the prior can be described as a specific Gaussian distribution by derivation. In our algorithm, a large scale of sophisticated training and time-consuming nearest neighbor searching is not necessary, and the cost function of this algorithm shows closed form solution. The experiments conducted on BSD500 and other popular images demonstrate that the proposed method outperforms traditional methods and is competitive with the current state-of-the-art algorithms in terms of both quantitative metrics and visual quality.

Landweber Iteration-Inspired Network for Image Super-Resolution

“…Image processing and analysis is an important task for signal processing [24,25]. As a classical issue in image processing, the task of image super-resolution (SR) is to generate highresolution (HR) images from low-resolution (LR) instances [26][27][28]. In recent years, convolutional neural networks (CNNs) have demonstrated amazing performance on image SR. SRCNN [13] is the first CNN-based image super-resolution network that achieves a large improvement over traditional works.…”