Image noise removal using optimal deep learning-based noisy pixel identification and image enhancement

Abstract

ABSTRACT Image restoration is utilised to discard image noise without demolishing edge information. A new optimisation based deep model is devised for image enhancement with restoration. Here, the noisy pixel map is detected with Deep Residual Network (DRN). DRN training is carried out using the JayaBat algorithm, which was created by combining the Jaya optimisation method and the Bat algorithm (BA). The statistical model is then used to restore the noisy pixels. The neuro fuzzy model and the Image Enhancement Conditional Generative Adversarial Network (IE-CGAN) are taken into account while enhancing images. Here, the IE-CGAN training is performed with designed CAViaRJayaBat. The CAViaRJayaBat is designed newly by combining the CAViaR and JayaBat algorithms. The proposed CAViaRJayaBat-based IE-CGAN offered improved performance with the best Peak signal to noise ratio (PSNR) of 49.049 dB, the highest Second derivative measure of enhancement (SDME), 62.570 dB, and the highest structural similarity index (SSIM), 0.858.