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Article type: Research Article
Authors: Jaiseeli, C.; * | Raajan, N.R.
Affiliations: Department of Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India
Correspondence: [*] Corresponding author. C. Jaiseeli, Department of Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India. E-mail: cjaiseeli@ece.sastra.edu.
Abstract: Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods.
Keywords: Histogram equalization, super-resolution, CNN, subsample image, VDSR, residual
DOI: 10.3233/JIFS-219392
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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