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All Journal JISA (Jurnal Informatika dan Sains) Journal of Information Technology and Its Utilization
Muhamad Ghofur
President University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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COMBINING SUPER-RESOLUTION ALGORITHM (GAUSSIAN DENOISING AND KERNEL BLURRING) AND COMPARING WITH CAMERA SUPER- RESOLUTION Muhamad Ghofur
Journal of Information Technology and Its Utilization Vol 4, No 1 (2021)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.4.1.3914

Abstract

A good Super Resolution (SR) algorithm is one of the key successes to filter frequency that creates noise to a picture. Previous research that has published was concluded the Camera SR is the best algorithm to filter this frequency based on their Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) results. However, the current approach to achieving high resolution have not yielded enough signal to filter unwanted pixel. Hence, there is a need to find a better approach to those leads to higher resolution through lower noise reduction. To fulfill this need, this thesis proposed to utilize two proven SR algorithms; Gaussian Denoising and Kernel Blurring. This thesis will not only be obtaining these two existing algorithms in a stand-alone form but hence the combination of them (two combinations) will also be obtained as the new possible algorithms that can be utilized to filter frequency that create noise to a picture. To reach the research objective, the method that will be used is by training a total of four algorithms one by one to a public data set that contains 200 pictures and gets the PSNR and MSE results of each algorithm. Comprehensive experimental results show that all those four SR algorithms outperform previous SR algorithms in commonly used data set with variously higher PSNR by 21% and lower MSE by 5%.
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution Muhamad Ghofur; Tjong Wan Sen
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.914

Abstract

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.
Co-Authors Tjong Wan Sen