An Iterative Image Inpainting Method Using Mask Shrinking

In the field of computer vision, image inpainting plays a crucial role in filling in missing portions of images. Current methods often utilize deep learning and GANs but may exhibit inconsistencies in output quality. To address this issue, we propose an iterative approach, involving multiple inpainting passes to progressively enhance incomplete areas. Additionally, we incorporate a quality assessment step to resize the mask image when necessary. Our method significantly improves accuracy, a fact validated through empirical results obtained from selected images and masks, making it a valuable addition to the existing techniques.