Single Image Super Resolution by No-Reference Image Quality Index Optimization in PCA subspace
Principal Component Analysis (PCA) has been effectively applied for solving atmospheric-turbulence degraded images. PCA-based approaches improve the image quality by adding high-frequency components extracted using PCA to the blurred image. The PCA-based restoration process is similar with conventional single-frame Super-Resolution (SR) methods, which perform SR process by improving the edges portion of low-resolution images. This paper aims to introduce PCA-based restoration to solve SR problem with additive white Gaussian noise. We conducted experiments using standard image database and show comparative result with the latest deep-learning SR approach.
Index Terms—Image Quality Assessment, Super Resolution, Single Image, Principal Component Analysis, Noise Robustness.