Performance analysis on dictionary learning and sparse representation algorithms

doi:10.1007/s11042-022-12375-4

2022-03-03

PRE

Suit Mun Ng

Haniza Yazid *

Nazahah Mustafa

DOI：10.1007/s11042-022-12375-4

原文链接

原文求助

摘要

En 中文

Theoretically, the Super-Resolution (SR) reconstruction scheme is a method which is performed by many applications nowadays for the purpose of generating a High-Resolution (HR) image using the input Low-Resolution (LR) images by filling in the missing high frequency information. In addition, the SR reconstruction implemented based on the theory of sparse representation techniques is known as an effective way to produce HR images using images patches generated from the LR images. In order to improve the quality of denoised images produced by using the sparse representation techniques, a scheme called dictionary learning algorithms could be considered. Thus, the objective of this paper is to provide a performance comparison on the effectiveness of applying the dictionary learning steps with sparse representation algorithms in producing a better denoised image. In this case, the average Peak Signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) values of the denoised image obtained by using Algorithms 1, 2, and 3 which combined the use of dictionary learning and sparse representation algorithms were compared with the values obtained from images produced by applying only sparse regularisation methods. As a conclusion, the denoised images produced by Algorithm 1 in this paper had the greatest average PSNR and SSIM values. Hence, the algorithm with the implementation of the dictionary learning process with sparse representation methods is able to achieve a better result in enhancing the low-resolution images.

Keyword:

Image processing

Super-resolution (SR)

Dictionary learning

Sparse representation