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The area of underwater image processing has gained huge attention among the researchers because of its wide application. Image processing is the approach of developing the input image quality so that it would be understood easily by users in future. Image processing develops the data content of image and changes the images visual impact on the observer. Image processing intensifies the image features and accentuates the features of image like corners, contrast to enhance the photographs display which is much helpful for study and examination. Several methods and techniques have been used by researchers worldwide in order to resolve the underwater image processing issues. This paper reviews different techniques of underwater image processing and also compares the techniques among each other. This paper discusses about the filtering method, histogram-based equalization method and particle swarm optimization techniques and compares them with their advantages.

Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.

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