Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.

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, [Formula: see text]-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[Formula: see text]dB, maximal SDME of 75.70[Formula: see text]dB and the maximal SSIM of 0.983.