Abstract
Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.
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