Comparing Effectiveness of GAN and CLAHE for Enhancing Underwater Images

Underwater image enhancement is a necessary process to improve the quality of images captured in underwater environments. The underwater environment presents unique challenges that can significantly impact the quality of images, including reduced light and color, distortion and blurring, limited visibility and reflections, and the effects of particles and debris. Enhancing underwater images can help to overcome these challenges and improve the visibility of the captured scene. This can be especially important in applications such as underwater exploration, marine biology, and underwater archaeology, where clear and detailed images are crucial for analysis and decision-making. This study compares Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generative Adversarial Networks (GAN). Images generated from both algorithms are compared on the basis of Peak Signal Noise Ratio (PSNR) values. PSNR value of image generated by GAN is greater than PSNR value obtained by CLAHE, so in order to obtain a highly enhanced underwater image GAN will be suitable.