Reconv: Repeated Convolutional Strategies for Advanced Image Inpainting

Abstract

Image inpainting is a promising but challenging approach that fills in huge free-form empty areas in images. Most of the recent papers concentrate on splitting masked image into two matrices of valid and invalid elements which makes the system more complex. This paper proposes a novel algorithm named ReConv which uses a repeated standard convolution operation which treats valid and invalid elements of an image in the same manner. The outcomes of our suggested method, ReConv, shows that, in comparison to earlier approaches, our system produces outputs that are more adaptable with good quality for real world applications. Our suggested technique enables users quickly modify faces, eliminate distracting items, change image layouts, and remove unwanted text. An extensive comparison study on two types of datasets validates our method. The effectiveness of the suggested strategy was evaluated using different measures such as PSNR, SSIM and FID. The results show that our recommended approach excels in performance compared to the existing modern methods.