Image restoration model using Jaya‐Bat optimization‐enabled noise prediction map

Abstract

Abstract Image restoration approaches are introduced to restore the latent clear images from the degraded images. However, the performance of the existing approaches remains an open problem, which may leads to the further development of advanced image restoration techniques. Therefore, an effective image restoration method is developed for restoring the input image from various noises, like impulse noise and random noise. The generation of pixel map, identification of noisy pixel, and the enhancement of pixel are the three major phases involved in the proposed method. Initially, the noisy pixel map generation is performed from the input image, and then the noisy pixels are identified based on deep convolutional neural network, which is trained by the proposed Jaya‐Bat algorithm. The Jaya‐Bat algorithm is developed by combing the Jaya optimization algorithm and Bat algorithm. Once the noisy pixels are identified, the pixel enhancement is done using the neuro fuzzy system. The experiment is carried out using Statlog (landsat satellite) dataset, and the developed method achieves the maximal peak signal to noise ratio of 51.03 dB, maximal structural similarity index of 0.848 for the image with random noise, and the maximal second derivative like measure of enhancement 62.96 dB with impulse noise, respectively.

Keywords

• Optical, image and video signal processing
• Optimisation techniques
• Computer vision and image processing techniques
• Other topics in statistics
• Optimisation techniques
• Other topics in statistics