Performance analysis on dictionary learning and sparse representation algorithms

• Published in: Multimedia tools and applications Vol. 81; no. 12; pp. 16455 - 16476
• Main Authors: Ng, Suit Mun, Yazid, Haniza, Mustafa, Nazahah
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2022; Springer Nature B.V
• Subjects: Algorithms; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Dictionaries; Image enhancement; Image quality; Image resolution; Machine learning; Multimedia Information Systems; Noise reduction; Reconstruction; Regularization; Representations; Signal to noise ratio; Special Purpose and Application-Based Systems; Sparse representation; Image processing; Super-resolution (SR); Dictionary learning
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12375-4

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.