Role of Preprocessing Algorithm in the Underwater Image Analysis

Various fields, such as marine biology, environmental monitoring, and underwater robots, all heavily rely on underwater image analysis. However, due to issues such as water turbidity, scattering, and light absorption, the quality of underwater photographs is greatly diminished, making conventional image processing approaches less efficient. To improve the quality and extract useful information from underwater photographs, preprocessing procedures must be used. The critical function of preprocessing techniques in underwater picture analysis is reviewed in this work. This chapter discusses how important noise reduction and distortion removal techniques are when processing underwater images. The effectiveness of adaptive filtering, wavelet denoising, and other pertinent approaches is discussed to reduce the negative effects of noise and distortions produced during image acquisition. This chapter explores how feature extraction, object recognition, and classification in underwater picture analysis are affected by pretreatment approaches. Preprocessing improves the effectiveness of these subsequent processes while also enabling a more thorough grasp of the undersea environment by enhancing the quality of input images.