Image completion with multi-image based on entropy reduction
In this study, we present a new image completion method based on image entropy reduction. We complete the missing region with semantically matching images, which maximizes the reduction in the combined entropy of all regions in the image. We use labeled regions (high confidence regions) to complete the uncertain regions. By contrast, existing image completion methods focus on simple filling and ignore creative and semantic matching completion. Entropy reduction can yield higher accuracy of semantic image matching than existing image completion methods. We use Poisson blending and blending optimization (color handling) to complete the missing region with higher-quality results. The superiority of our method to existing image completion algorithms is validated. Experiments using an image database show that our method significantly improves the completion results.
image completion image entropy semantic matching joint entropy gist sift