Analysis on Single-Image Super-Resolution (SISR) Using Dictionary Learning and Sparse Representation Algorithm

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.