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Progressive edge-sensing dynamic scene deblurring
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AI
DOI：10.1007/s41095-021-0246-4.png)
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Abstract
En 中文
Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors. In recent years, the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied. We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams. Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges. The coarse-to-fine architecture restores the image structure, first performing global adjustments, and then performing local refinement. In this way, not only is global correlation considered, but also residual information is used to significantly improve image restoration and enhance texture details. Experimental results show quantitative and qualitative improvements over existing methods.
Keywords:
image deblurring
dynamic scenes
multi-scale
edge features
Journal
IF:
18.3
Papers:
310
Citations:
2.6K
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