Syafiq Qhushairy Bin Syamsul Amri creator author. text my 2025 2025 monographic eng xiv, 125 pages : illustrations (some color) ; 30 cm. + 1 CD-COM Underwater image enhancement is crucial for improving visual perception and the applicability of underwater images. Various methods have been proposed to enhance underwater images, such as modeling image enhancement as a distortion process and utilizing color constancy-based schemes. Challenges in underwater image processing include factors like turbidity, light refraction, and low-contrast objects, which impact image quality and cause unnatural views and disrupted object colors in the underwater environment. The objective in this thesis is to develop robust image processing algorithms that can effectively enhance underwater images and improve visibility for various applications. This will help researchers and professionals in fields such as marine biology and underwater photography to obtain detail and improve visibility images of underwater scenes. The second objective is to analyse and verify the developed algorithm for improvement of images color cast and object details, ultimately leading to image quality. The method suggested in this thesis is the Integrated Chromatic Adaptation Modified White Balance (CAMWB), which aims to enhance visibility and object detail in underwater images. This work focuses on addressing the primary difficulties of visibility and object detail in the underwater environment, as the existing methods still require assistance in effectively resolving these issues. This work has developed algorithms based on mathematical equations, leading to the creation of a novel three-stage CAMWB technique. In the first stage, the modification process using chromatic adaptation-based image color channel neutralization is implemented to improve and correct the color balance of the underwater image. The second stage is image intensity mapping on a dual image histogram, which is used to improve the performance of the less effective color channels. Lastly, brightness reconstruction for each color channel is used in the last stage to enhance the overall object color and achieve a more realistic representation of the underwater scene by adjusting the contrast and shadows. The findings indicate that the CAMWB implementation method effectively resolved particular problems related to visibility, object color cast, and image details. Additionally, it enhanced the overall quality of underwater images by enhancing uniform illumination and image contrast. The proposed approach demonstrates superior performance in extracting information from underwater images. Experiments conducted on 300 sample underwater images taken from Redang Island in various scenarios demonstrate that the proposed CAMWB method enhances the visual quality of output images and surpasses existing state-of-the-art methods such as integrated color model (ICM), the natural-based underwater image color enhancement through the fusion of swarm intelligence algorithm (NUCE), the dual-intensity images and Rayleigh stretching (DIRS), the bio-inspired multi-exposure fusion framework for low light image enhancement (BIMEF), the Rayleigh contrast-limited adaptive histogram equalisation (Rayleigh CLAHE), the Histogram Equalisation (HE), and the color balance and fusion for underwater image enhancement (WB Multiscale Fusion). The results indicate that CAMWB outperforms other methods in terms of average gradient value, with a value of 9.201. In conclusion, the Integrated Chromatic Adaptation Modified White Balance (CAMWB) algorithm developed in this study significantly improves underwater image quality by addressing critical challenges such as reduced water clarity, light attenuation, and color distortion. specialized Syafiq Qhushairy Bin Syamsul Amri Faculty of Manufacturing and Mechatronic Engineering Technology Thesis (Master of Science) -- Universiti Malaysia Pahang – 2025 Includes bibliographical references Faculty of Manufacturing and Mechatronic Engineering Technology Dissertations Universities and colleges Dissertations Theses Dissertations THE0010207 (Local) UMPSA 260101 20260101143852.0 eng