Image inpainting via Multi-scale Adaptive Priors

Abstract

Image inpainting aims to fill the corrupted regions of an image while maintaining global consistency. Many image inpainting methods have made significant progress by incorporating reconstructed single-scale or multi-scale simplified image information as priors to provide explicit structural or textural assistance during the inpainting process. However, these methods typically design priors manually as predefined types of image information based on intuitive choices, overlooking the incomprehensible variations in information that image feature recovery at different scales tends to focus on, which inevitably reduces the efficiency of priors in assisting image feature recovery, and may result in suboptimal performance. To address this issue, we propose Multi-scale Adaptive Priors (MAPs), which dynamically adjust information based on assisting image features at each scale. MAPs are obtained through the MAPs Reconstructor (MAPs-R), which sequentially extracts, reconstructs, and adaptively aggregates multi-scale image representations from corrupted images. To explore MAPs’ potential in assisting inpainting, we designed the MAPs-based Inpainting Network (MAPs-IN), branching feature recovery at each decoder stage to focus on different information levels. Experimental results demonstrate that our proposed priors can more effectively assist in image feature inpainting and ultimately outperform other inpainting methods.