Patch-based Texture Synthesis for Image Inpainting
This addresses the problem of filling missing or damaged regions in images for image processing and vision applications, but it appears incremental as it builds on existing patch-based approaches.
The paper tackles image inpainting by developing a patch-based texture synthesis method that finds optimal candidate patches and generates new textures, achieving high accuracy and desirable outputs for applications like object removal and background subtraction.
Image inpaiting is an important task in image processing and vision. In this paper, we develop a general method for patch-based image inpainting by synthesizing new textures from existing one. A novel framework is introduced to find several optimal candidate patches and generate a new texture patch in the process. We form it as an optimization problem that identifies the potential patches for synthesis from an coarse-to-fine manner. We use the texture descriptor as a clue in searching for matching patches from the known region. To ensure the structure faithful to the original image, a geometric constraint metric is formally defined that is applied directly to the patch synthesis procedure. We extensively conducted our experiments on a wide range of testing images on various scenarios and contents by arbitrarily specifying the target the regions for inference followed by using existing evaluation metrics to verify its texture coherency and structural consistency. Our results demonstrate the high accuracy and desirable output that can be potentially used for numerous applications: object removal, background subtraction, and image retrieval.