Universiti Teknologi Malaysia Institutional Repository

Single image Super Resolution by no-reference image quality index optimization in PCA subspace

Sumali, B. and Sarkan, H. and Hamada, N. and Mitsukura, Y. (2016) Single image Super Resolution by no-reference image quality index optimization in PCA subspace. In: 12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016, 4 March 2016 through 6 March 2016, Melaka; Malaysia.

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Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Image Quality Assessment, Noise Robustness, Principal Component Analysis, Single Image, Super Resolution
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:73126
Deposited By: Muhammad Atiff Mahussain
Deposited On:29 Nov 2017 23:58
Last Modified:29 Nov 2017 23:58

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