Open AccessCombining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution
Author(s) -
Muhamad Ghofur,
Tjong Wan Sen
Publication year - 2021
Publication title -
jurnal informatika dan sains/jisa (jurnal informatika dan sains)
Language(s) - English
Resource type - Journals
eISSN - 2776-3234
pISSN - 2614-8404
DOI - 10.31326/jisa.v4i2.914
Subject(s) - resolution (logic) , algorithm , computer science , superresolution , artificial intelligence , computer vision , image resolution , pixel , kernel (algebra) , limit (mathematics) , image (mathematics) , mathematics , combinatorics , mathematical analysis
This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms.