Underwater Image Enhancement Using Hybrid Techniques

Underwater image enhancement is a crucial task in improving the visibility, quality, and details of images captured in aquatic environments. This study presents an overview of various techniques and methodologies employed for underwater image enhancement. This study also discusses the challenges associated with underwater imaging, including color distortion, low contrast, and the effects of light scattering and absorption. It highlights the importance of preprocessing steps such as color correction and noise reduction. The study also emphasizes the effectiveness of adaptive histogram equalization (AHE) and contrast-limited adaptive histogram equalization (CLAHE) in enhancing local contrast and details. Additionally, it mentions advanced techniques like dehazing, fusion, and restoration algorithms that further enhance image visibility and remove artifacts. This research emphasizes the need for incorporating underwater image priors and exploring deep learning approaches for more accurate and robust underwater image enhancement. This work proposes a gray world technique for white balance. From the corrected image, weight maps-based feature extraction would be done followed by multi-fusion and image enhancement. Due to the wide application of wavelet transform in image processing, here using two-dimensional Discrete Wavelet Transform as a fusion operator. Also, make the synaptic network a new level of application. The result demonstrates that the proposed enhancement algorithm can obtain a well visual result