Cr-Coot: Chronological Coot Algorithm-Based Deep Learning Model for Video Demosaicing

Abstract

Usually, digital cameras comprise sensor arrays enclosed by Color Filter Arrays (CFAs), mosaics of minute color filters. Thus, every pixel sensor usually records limited spectral data regarding relevant pixels. Demosaicing is defined as the procedure of deducing the misplaced data for every pixel, which plays a vital role in recreating high-quality full-color images. Denoising and demosaicing are the major processes in the camera imaging chain for both videos and images. Here, reconstruction errors occur in these points and have undesirable effects on the final outcome, when it is not appropriately managed. The demosaicing process provokes color and spatial correlation of noises, and it is improved by means of imagining a pipeline. This organized noise usually destroys the quality of the image as well as fails to prevent accurate interpretation of an image. During the mitigation of this structured noise on processed data, denoising techniques diminish the texture and information. Therefore, an effectual demosaicing technique is essential for recreating the full-color image from the defective color samples. Thus, in this paper, an effectual video demosaicing model is proposed using an optimized deep learning system. The designed video demosaicing system achieved better performance with a Peak Signal-to-Noise Ratio (PSNR) of 59.74 dB, Second Derivative, like Measure of Enhancement (SDME) of 63.51, and Root Mean Squared Error (RMSE) of 0.3660.