An Image Inpainting Method Based on Whale-Integrated Monarch Butterfly Optimization-Based DCNN

Abstract

This paper proposes an image inpainting method based on Whale integrated Monarch Butterfly Optimization-based Deep Convolutional Neural network (Whale-MBO-DCNN) model. Initially, the patch extraction and mapping are applied to the input image to extract the patches of the image followed by image reconstruction in order to map the patches. The patch with minimum distance is selected using the concept of Bhattacharya distance in patch extraction. On the other hand, the construction of the residual image form the input image is done using Deep CNN, which is trained with the proposed Whale-MBO algorithm. The proposed Whale-MBO algorithm is developed from the integration of Monarch Butterfly Optimization (MBO) and (WOA. Finally, the residual image and the reconstructed image are fused using Holoentropy to obtain the reconstructed image. The experimentation is performed using the evaluation metrics, such as PSNR, SDME, and SSIM. The effectiveness of the proposed image inpainting method is revealed through a higher PSNR, SDME, and SSIM of 33.0585, 74.4249, and 0.9479, respectively.