Enhancing Image Inpainting With Deep Learning Segmentation and Exemplar-Based Inpainting

The technique of recreating faded or lost portions of an image is called image inpainting. A critical challenge in image inpainting is accurately identifying the areas that need reconstruction. This article explores the integration of deep learning segmentation to enhance the efficiency of image inpainting and exemplar-based inpainting methods using a two-stage structure tensor and image sparse representation to fill in missing areas. By leveraging advanced segmentation models, we can precisely delineate the areas requiring inpainting, allowing for more seamless and realistic restorations. Together, the exemplar-based inpainting method involves selecting filling order, maintaining structure, and blending candidate patches for natural results in object removal. Because we are using actual photographs, we do not compare between images after fill and solution. Therefore, we use the Mann–Whitney $U$ test to compare efficiency approaches.