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

Super resolution algorithms attempt 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. This article introduces a preprocessing operation to improve the performance of the super resolution process. In the proposed method, the low-resolution images are enhanced before entering to 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 ۴.۳۵%, ۱۰.۶۲%, and ۸.۳۱%, as well as ۰.۲۳%, ۳.۱۰%, and ۷.۹۱% of the SSIM parameter for Set۵, Set۱۴, and BSD۱۰۰ benchmark datasets compared to its closest state-of-the-art methods.

Keywords:

Single Image Super Resolution , Natural Images , Luminance Map , GaN , convolutional neural network