Flower pollination student psychology optimization-integrated context deep learning and probabilistic-based fusion for image inpainting

Abstract

Image inpainting is the process to fill missing pixels in the damaged image and this process has drawn more attraction and gained active and expensive research topic in recent decades, because the high quality in the image inpainting benefits a greater range of applications, like object removal, photo restoration, and so on. Inpainting of larger quality of the image needs to fill the empty regions with plausible content in the damaged image. The existing inpainting methods either fill image regions by stealing the image patches or semantically create coherent patches from the regional context. Most of the traditional models perform well on small holes images, but restoring the image with large holes still results a challenging task. To overcome such issues and to generate effective inpainting results, a proposed method named the hybrid context deep learning approach is designed in order to fill empty regions of crack images. Moreover, the proposed method is more effective by employing a hybrid optimization algorithm for training of classifier to generate a more robust and accurate inpainted result. The developed model includes two different deep learning classifiers to accomplish the process of image inpainting in such a way that the results are fused through the probabilistic model. Moreover, the proposed approach attains higher performance by the metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative like Measure of Enhancement (SDME), and Universal Quality Index (UQI) with the values of 38.02[Formula: see text]db, 0.867, 54.32[Formula: see text]db, and 0.864, respectively.