Abstract
Image inpainting is an evolving discipline of image processing with the objective of reconstructing an image by removing unwanted information, adding missing information or presenting the information appealing to the human visual system. In the presented manuscript, we have exhibited an extensive survey of various image inpainting techniques. The effectiveness of the techniques is together summarized with significant comparisons and assessed by analyzing the merits and demerits. For applicability of image inpainting imparting optimum results in the field of loss concealment, object removal, image restoration or disocclusion, the information from nearby regions is seeked to acquire an image with restored absent information. The inpainted image result can be evaluated using subjective and objective analysis, with emphasis on subjective analysis as a dedicated tool for evaluation.
Highlights
Image inpainting is a branch of image processing which aims at reconstruction of a distorted image (Pushpalwar and Bhandari, 2016)
We conferred a comprehensive review of various image inpainting techniques with an extensive survey on accentuating advantages and disadvantages for each of the technique presented
With the development and evolution of plethora of techniques in the domain of image inpainting, the concern lies in specifying a particular technique as the relevant one
Summary
Image inpainting is a branch of image processing which aims at reconstruction of a distorted image (Pushpalwar and Bhandari, 2016). -Automatic image inpainting by just specifying the area to be inpainted -Aims at recovery of geometrical information -Quality and speed of the algorithm is better than the other techniques -Works good for totally different structures -A successful technique by the union of image decomposition along with structure and texture synthesis -Image inpainting with the union of transformations -Adjusts well to a particular image even if nearby local regions are same -Edge preservation -Fast -Efficient. -Fast -Better visual results -Works well on inconsistent holes -Efficient -Better image inpainting quality -Works well for simple structures and large absent areas Reduced artifacts -Works on holes with different resolutions. -Added discontinuities and artifacts -Not applicable to images with different colors and textures -Unable to handle complex structures Doesn’t work on higher resolutions
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