A Hybrid Deep Learning and Color Space Model for Digital Image Enhancement
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22658Abstract
Image contrast enhancement is crucial in digital image processing. Enhancing contrast highlights image details, improves lighting, and focuses on key features that help maintain image quality and enhance visual perception in various fields such as computer vision, medical imaging, and surveillance systems. Deep learning models are applied to improve the contrast and brightness of digital images while preserving fine details and minimizing distortion. This article presents a method for improving image appearance using a hybrid application of deep learning and digital image processing techniques with different color systems to obtain an enhanced image from the original. Three color models and four deep learning techniques are used with contour transformations to capture the finest details of the digital image, in addition to applying contrast enhancement techniques to increase image clarity. The color system of a color image is converted from RGB to other color systems. The color system is then divided into its original layers, which are further deconstructed using contour transformations. Contrast enhancement techniques are then applied to increase the contrast in the digital image, thus training the model to optimize image colors. A range of metrics were also used to measure the generalizability of the proposed system (PSNR, SSIM, accuracy). The results showed that the best color system was HSV, when combined with the ResNet50 deep learning model. The results were as follows: PSNR = 44.2, SSIM = 0.9952.
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Copyright (c) 2026 Sarah M. Mahdi, Ahmed S. Abdullah
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