An Intelligent Underwater Image Color Correction Principles Using Modified Filtration Array With Distinct Pixel Evaluation Strategy

Due to a number of circumstances, including absorption, scattering, and the presence of suspended particles, etc., underwater images typically experience quality deterioration and aberrations. In order to tackle these issues, underwater vision tasks use a suite of technologies for improving and restoring underwater images. A variety of techniques, such as filtration, colour modification, combining images, and deep acquiring knowledge, may be utilized to enhance underwater photographs and these techniques are only some of the many options available. Within the scope of this article, we will take a look at various methods for improving images, and we present one called Modified Filtration Array based Pixel Evaluation (MPAPE). To test how well it works, we will use a cross-validation procedure with the traditional Color Correction based Filter Array (CCFA) model. With the enhancements suggested, the algorithm can save more texture information and edge characteristics while still being able to reduce noise. To start, the suggested MPAPE model improves color by adjusting for color shift. Secondly, to address the issue of low contrast, through the use of the adaptive masking factor together with the regional unsharp disguise, we implement to distinguish between the foreground and background. This increases picture contrast while simultaneously improving underwater object and background identification. The objective is to combining the four weights complete the balanced merging of the stochastic pyramidal structure. According to various independent metrics, the method suggested in this paper achieves the best outcomes, and the experimental findings demonstrate that it has strong generalizability and may enhance the clarity of the image in various scenes involving water.