• Nelavai jyothika
• Jalakam sreya nidhi

Underwater image enhancement has long been a challenging task due to issues such as color distortion, low contrast, and blurriness caused by light absorption and scattering in water. Traditional image processing techniques often fail to deliver high-quality results in such conditions. This paper compares two advanced deep learning algorithms for underwater image enhancement: ResNet (Residual Networks) and Generative Adversarial Networks (GANs). The focus is to assess their performance in terms of visual quality, preservation of details, and recovery of color accuracy. The study shows that both algorithms offer promising results but highlight the strengths and limitations inherent to each approach.

underwater image enchancement,resnet

"UNDERWATER IMAGE ENCHANCEMENT USING ResNET ALGORITHM COMPARED WITH GENERATIVE ADVERSARIAL NETWORKS ALGORITHM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.g656-g661, December-2024, Available :http://www.jetir.org/papers/JETIR2412674.pdf

"UNDERWATER IMAGE ENCHANCEMENT USING ResNET ALGORITHM COMPARED WITH GENERATIVE ADVERSARIAL NETWORKS ALGORITHM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppg656-g661, December-2024, Available at : http://www.jetir.org/papers/JETIR2412674.pdf