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Haniza Yazid
Analysis on Single-Image Super-Resolution (SISR) Using Dictionary Learning and Sparse Representation Algorithm
Performance analysis on dictionary learning and sparse representation algorithms
2022-01-01
,
Ng S.M.
,
Haniza Yazid
,
Mustafa N.
Image Super-Resolution (SR) is a technique in order to produce High-Resolution (HR) image from the corresponding Low-Resolution (LR) image by removing the degradation caused by imaging process of LR camera. In this work, a Single-Image Super-Resolution (SISR) image reconstruction scheme based on dictionary learning process with sparse representation method is proposed. As a result, the image quality of the obtained HR image decreased significantly with increasing of the upscale factor. Then, the analysis showed that the HR image obtained by applying the proposed work was able to produce a better performance in terms of Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Matric (SSIM) values as compared to the bicubic interpolation operation. Therefore, the work done in this paper is able to solve the LR problem in images by proposing a SISR image reconstruction scheme based on dictionary learning process with sparse representation algorithm. Lastly, this work can be improved by testing on different types of images such as biometric images.
2022-05-01
,
Ng S.M.
,
Haniza Yazid
,
Mustafa N.
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.