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Dual optimization image repair algorithm based on linear structure and optimal texture
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AI
DOI：10.1007/s11771-014-2183-1.png)
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Abstract
En 中文
The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content. Image repair algorithm with texture information performs well in repairing seriously damaged images, but it has bad performances when the images have the abundant structure information. The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed. The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images. After adopting the preference of Criminisi priority, the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image. The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness, and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.
Keywords:
image restoration
linear structure
texture information
iteration
sparse representation
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