Master's Theses or Doctor of Nursing Practice

Department

Computer Science

Degree Name

Master of Science (MS)

Abstract

Low-light image enhancement is a major challenge in digital imaging, especially in medical imaging, surveillance, and autonomous vision systems. Images captured under poor illumination often appear dark, noisy, and low in contrast, which makes it hard to observe important details. Traditional enhancement methods can improve brightness but usually introduce artifacts or increase noise. Although deep learning methods have shown strong performance, they usually require large datasets, and high computational resources. This creates a need for simpler and more efficient enhancement techniques. This study proposes a lightweight framework that incorporates Gaussian denoising with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance low-light and noisy images. Gaussian denoising decreases high-frequency noise, while CLAHE improves local contrast and visibility. The method is applied to both grayscale and color images, with color enhancement performed in the LAB color space. An evaluation is done on the framework using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Lightness Order Error (LOE), together with visual analysis. Results show that the method improves image brightness, enhances contrast, and reduces visible noise, producing clearer and more interpretable images. Overall, it is confirmed in the findings that Gaussian denoising combined with CLAHE provides an effective and computationally efficient solution for low-light image enhancement.

Keywords

Retinex theory, spatial filtering, luminance processing, pixel intensity redistribution, VGG16 validation

Advisor

Dr. Anas Hourani

Date of Award

Spring 2026

Document Type

Thesis

Rights

© The Author

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