Advancements in Underwater Image Enhancement via Deep Convolutional Neural Networks: A Comprehensive Study - Ghanshyam Sahu (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research explores progressions in submerged picture improvement utilizing profound convolutional neural networks (CNNs) and related strategies. Through the assessment of calculations such as U-Net, Profound Retinex-Net, EnhanceGAN, and CycleGAN, our study investigates their viability in progressing picture quality beneath challenging submerged conditions. Exploratory comes about illustrate that Profound Retinex-Net accomplishes the most noteworthy top signal-to-noise proportion (PSNR) of 34.2 dB and the most elevated basic likeness file (SSIM) of 0.87, exhibiting predominant execution compared to other calculations. U-Net moreover performs well, accomplishing a PSNR of 32.5 dB and an SSIM of 0.85. EnhanceGAN and CycleGAN show somewhat lower PSNR and SSIM scores, demonstrating comparatively lower constancy and basic likeness. Through a comprehensive survey of related work, this investigate contextualizes the discoveries inside the broader scene of submerged imaging investigate, recognizing key patterns and future inquire about bearings. In general, this study contributes to the headway of submerged imaging innovation, with suggestions for marine science, investigation, and preservation.