论文标题
参考引导的纹理和图像插图的结构推理
Reference-Guided Texture and Structure Inference for Image Inpainting
论文作者
论文摘要
当面对复杂的语义环境和各种孔模式时，现有的基于学习的图像介绍方法仍在挑战。从大规模培训数据中学到的先前信息仍然不足以解决这些情况。捕获的覆盖相同场景的参考图像与损坏的图像共享相似的纹理和结构先验，该图像为图像插入任务提供了新的前景。受此启发的启发，我们首先构建了一个基准数据集，其中包含10k对的输入和参考图像，以引入引导介绍。然后，我们采用编码器折叠结构来分别推断输入图像的纹理和结构特征，以考虑其在indpaining期间的纹理和结构的模式差异。进一步设计了特征对齐模块，以通过参考图像的指导来完善输入图像的这些特征。定量和定性评估都证明了我们方法在完成复杂孔方面的优越性。
Existing learning-based image inpainting methods are still in challenge when facing complex semantic environments and diverse hole patterns. The prior information learned from the large scale training data is still insufficient for these situations. Reference images captured covering the same scenes share similar texture and structure priors with the corrupted images, which offers new prospects for the image inpainting tasks. Inspired by this, we first build a benchmark dataset containing 10K pairs of input and reference images for reference-guided inpainting. Then we adopt an encoder-decoder structure to separately infer the texture and structure features of the input image considering their pattern discrepancy of texture and structure during inpainting. A feature alignment module is further designed to refine these features of the input image with the guidance of a reference image. Both quantitative and qualitative evaluations demonstrate the superiority of our method over the state-of-the-art methods in terms of completing complex holes.
