Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors

The research presents a unique approach to the iterative pixel compression method for pixel optimization by reducing noise with a motion-guided backdrop. Image resolution and precision are increased by using a complementary metal oxide semiconductor (CMOS) image sensor. Researchers offer a dispersed equivalent implementation of the Iterative Pixel Compression technique for CMOS image sensors in order to successfully handle the expanded data. The current frame is handled by the buffer circuit in the CMOS image sensor. The registered bank is related to subsequent frames. It consists of a collection of registers that retain information on the grey levels of the acquired pictures' pixels. The image DE noising signal process is applied to the input picture, which contains noise. The pixel averaging filter is used in image DE noising to enhance picture quality and produce a better estimate. Pixel ordering identifies misplaced areas of photos due to the use of an iterative pixel reduction method. It allocates the best existing pixel feasible. Peak signal-to-noise ratio (PSNR) assess the image's quality through and Mean Square Error (MSE). When compared to previous approaches, our results demonstrate a 2% improvement in PSNR and a 1% reduction in MSE.