Bayes-Probabilistic-Based Fusion Method for Image Inpainting

Image inpainting removes unwanted objects from the image, signifying the original image restoration. Even though several techniques are introduced for image inpainting, but still, there are several challenging issues associated with the conventional methods regarding data loss, which are effectively handled based on the proposed approach. In this paper, we propose an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion-Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, K-nearest neighbors (KNN) and the harmonic functions. The crack input image is forwarded as an input to Markov random field modeling to obtain image inpainting, where the MRF energy is minimized based on the ALG. Then, the same crack image is subjected to the Whale-MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules to obtain the inpainting results. Finally, the results from the proposed ALG-based Markov random field modeling, Whale-MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules are fused through Bayes-probabilistic fusion for the final inpainting results. The proposed method produces the maximal PSNR of 38.14 dB, maximal SDME of 75.70 dB and the maximal SSIM of 0.983.

Automated summary

This paper proposes an effective hybrid image inpainting method that combines ALG, DCNN, KNN, and bi-harmonic function to achieve good results in image restoration.