Semi-independent Convolution for Image Inpainting

In typical image inpainting tasks, the locations and shapes of damaged or masked areas are often random and irregular. Vanilla convolutions, commonly employed in learning-based inpainting models, treat all spatial features as valid and share parameters across different regions. This approach can struggle with irregular damage patterns, leading to inpainted results that may suffer from color discrepancies and blurriness. In this paper, we introduce a novel operator known as Semi-Independent Convolution (SIConv) to tackle this challenge. The proposed SIConv, on top of the regular convolution with shared weights, also introduces dynamic terms that assign their own independent weights to each part of the image, and the overall computation is formulated as a shared convolution parameter with an additional term to describe the local structure. Qualitative and quantitative experiments demonstrate that our method outperforms the state-of-the-art, yielding clearer, more coherent, and visually convincing inpainting results.