Novel-Fl Operators Based Visibility Processing Techniques of Denoising Algorithm Using Exdark Dataset Simulation

Abstract

Abstract: An inclement dusty weather can significantly reduce the visual quality of captured images and this consequently leads to hamper the observation of meaningful image details. Capturing images in such weather often leads to undesirable artifacts such as poor contrast, deficient colors or color cast. Hence, various methods have been proposed to process such unwanted event and recover vivid results with acceptable colors. These methods vary from simple to complex due to the variation of the used processing concepts. In this article, an innovative technique that utilizes tuned fuzzy intensification operators is introduced to expeditiously process poor quality images captured in an inclement dusty weather. Intensive experiments were carried out to check the processing ability of the proposed technique, wherein the obtained results exhibited its competence in filtering various degraded images. Specifically, it performed well in providing acceptable colors and unveiling fine details for the processed images. This study expanded upon earlier methods of low-light picture enhancement that relied on DCP. After the light reflection direction was initialised, it was refocused using the red channel estimate. Separating the region with strong lighting characteristics from the background darkness zone is another usage of DCP. The next step was to create and assess new anthropogenic light quality by estimating and refining transmission maps. An image free of black areas is also produced by recovering the picture light radiance using the updated transmission map data. The last step in achieving high-quality output images is using the denoising algorithm. Results from the ExDark dataset simulations shown that the proposed strategy achieved better subjective and objective performance than state-of-the-art methods.