Single Image Super-Resolution Enhancement using Luminance Map and Atmospheric Light Removal

  • Mohammad Amin Shayegan Department of Computer, Shiraz Branch, Islamic Azad University, Shiraz, Iran
  • Samira Poormajidi
Keywords: Single Image Super Resolution, Natural Images, Luminance Map, GAN, Convolutional Neural Network


Image enhancement is used in many image processing applications such as medical diagnostics, satellite image analysis, surveillance cameras, etc. Super resolution attempts to reconstruct high resolution images from low resolution images and it can be considered as a preprocessing step for object recognition and image classification. Various algorithms have been introduced for single-image super resolution, but these algorithms often face important challenges such as poorly matching the reconstructed image with the original image, as well as the blurring of edges and texture details. The aim of this manuscript is to introduce a preprocessing operation to improve the performance of the super resolution process in natural images. In the proposed method, the low resolution input image is enhanced before entering the resolution change module. Calculating the brightness of the pixels in the image channels, creating the luminance map and removing atmospheric light, applying the transmittance map by using the luminance coefficients, and recovering the natural image in all three color channels are the above preprocessing steps. The proposed method succeeded in increasing the PSNR parameter by 4.35%, 10.62%, and 8.31%, as well as 0.23%, 3.10%, and 7.91% of the SSIM parameter for Set5, Set14, and BSD100 datasets compared to its closest state-of-the-art methods.


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How to Cite
Shayegan, M., & Poormajidi, S. (2022). Single Image Super-Resolution Enhancement using Luminance Map and Atmospheric Light Removal. Majlesi Journal of Electrical Engineering. Retrieved from