A Deep Learning Algorithm Grounded Image Dehazing for Corrupted Underwater Image Classification
Keywords:
CCSSR, NIQE, UIQM, ARCR, DCPAbstract
Salt marshes, coral reefs, the deep sea, and the seafloor are all parts of the marine ecosystem, which is the largest of Earth's aquatic ecosystems. The low quality of photographs captured underwater due to a number of degradations, however, has prevented this potential from being fully realised. The current research sheds insight on common issues with underwater images, such as colour shift, haze, dim lighting, uneven lighting, and poor contrast. When the blue colour wavelength is not absorbed in seas of sufficient depth, it typically leads to a bluish colour cast, which degrades underwater photographs. As a result, the colour information in marine photographs is compromised. The Colour Corrected single-scale Retinex (CCSSR) approach is used to color-correct underwater photographs, and the proposed work focuses on characterising the various ranges of the colour cast present in such photos. Additionally, an illumination enhancer helps bring out more detail in the underwater photo. Natural Image Quality Evaluation (NIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Underwater Image Quality Measure (UIQM), and entropy are only some of the non-reference quality measures used to assess the quality of the work that has been proposed. When compared to underwater images processed using the Automatic Red Channel Restoration (ARCR) method, the entropy of the proposed DCP MSR-based fusion is 24.2% higher, and the UIQM is 34.25 percentage points higher.
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