Inpainting has received a lot of attention in recent years and quality assessment is an important task to evaluate different image reconstruction approaches. In many cases inpainting methods introduce a blur in sharp transitions in image and image contours in the recovery of large areas with missing pixels and often fail to recover curvy boundary edges. Quantitative metrics of inpainting results currently do not exist and researchers use human comparisons to evaluate their methodologies and techniques. Most objective quality assessment methods rely on a reference image, which is often not available in inpainting applications. Usually researchers use subjective quality assessment by human observers. It is difficult and time consuming procedure. This paper focuses on a machine learning approach for no-reference visual quality assessment for image inpainting based on the human visual property. Our method is based on observation that Local Binary Patterns well describe local structural information of the image. We use a support vector regression learned on assessed by human images to predict perceived quality of inpainted images. We demonstrate how our predicted quality value correlates with qualitative opinion in a human observer study. Results are shown on a human-scored dataset for different inpainting methods.
A novel image inpainting algorithm based on edge reconstruction using combined approach capable to restore both image texture and structure is proposed in this paper. For edge and boundary detection and recovery a multistage edge detection procedure based on cubic splines is used. The choice of the current pixel to be recovered is decided using the fast marching approach. The Telea method or the exemplar based method are used after this depending on the classification of the regions where to-berestored pixel is located. The performance of the proposed approach is demonstrated via several examples, showing the effectiveness of the proposed algorithm in removal of small and large objects from the test images.
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