Open AccessAnalysis of feature representation in dictionary learning and sparse coding algortihms for low resolution image
Author(s) -
Suit Mun Ng,
Haniza Yazid
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012139
Subject(s) - artificial intelligence , pattern recognition (psychology) , sparse approximation , neural coding , feature (linguistics) , computer science , peak signal to noise ratio , biometrics , image (mathematics) , k svd , coding (social sciences) , singular value decomposition , image resolution , mathematics , computer vision , statistics , philosophy , linguistics
Super-Resolution (SR) is used to recover a high-resolution (HR) image from the image with low-resolution (LR). SR is important in the biometric identification and the face recognition is an area that bring attention to people. However, the performance of the current systems is affected by the resolution of the input images. Thus, this paper is focusing on the analysis of feature representations in dictionary learning and sparse coding methods for LR image. The input image is the Lena image in grey scale. A total number of 23 features were extracted from the image patches to develop different learned dictionaries using the k-singular value decomposition (k-SVD) algorithm. The denoised images were then produced by using the Douglas-Rachford algorithm. Most of the feature representations were able to produce a final image with Peak-to-Signal Noise Ratio (PSNR) and Structural Similarity Index Matric (SSIM) values of approximately 29 dB to 30 dB and 0.8300 to 0.8600 respectively. However, the denoised image produced with gradient direction obtained only 27.6676 dB and 0.7881 for PSNR and SSIM. Therefore, when different features were extracted for conducting the dictionary learning and sparse coding algorithm, denoised image with different PSNR and SSIM were produced at the end of the process.