Abstract: Now-a-days, a digital image can be found almost everywhere, and digital image processing plays a huge role in analyzing and enhancing the image so that it can be delivered in a good condition. Color distortion and loss of image details are the common problems that were faced by low-light image enhancement methods. This paper introduces a low-light image enhancement method that applied the concept of homomorphic filtering, unsharp masking, and gamma correction. The aim of the proposed method is to minimize the two problems stated while producing images of better quality when compared to the other low-light image enhancement methods. An objective evaluation was done on the proposed method, comparing the results produced by the enhanced method with other two existing low-light image enhancement methods. The results obtained showed the proposed method outshines the other two existing low-light image enhancement method in maintaining the image details and producing a natural looking image, achieving the lowest Mean Square Error (MSE) and Lightness Order Error (LOE) scores, and has the highest Features Similarity Index color (FSIMc), Features Similarity Index (FSIM), Structure Similarity Index (SSIM), and Visual information fidelity (VIF) scores. Future studies that should be made on this research are to implement dehaze and denoise functionality into the low-light image as well as enabling it to be applicable in real-time scenarios.

Tan Wan Yin, Kasthuri A/P Subaramaniam, Abdul Samad Bin Shibghatullah and Nur Farraliza Mansor, “Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130864

@article{Yin2022,
title = {Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130864},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130864},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Tan Wan Yin and Kasthuri A/P Subaramaniam and Abdul Samad Bin Shibghatullah and Nur Farraliza Mansor}
}