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Dark Channel Based Multiframe Super-Resolution Reconstruction
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AI
DOI：10.1109/ACCESS.2021.3120058.png)
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Abstract
En 中文
Multiframe super-resolution (MFSR) can obtain a high-resolution image from a set of low-resolution images. The performance of super-resolution is affected by the image prior information. The current super-resolution algorithms typically use total variation prior and its improved version, restoring the image edges well. However, it will produce artifacts and stair effects in the smooth region of the image. Therefore, we propose a dark channel-based MFSR algorithm to achieve edge-preserving and noise-suppressing. Firstly, the total variation prior is used to ensure the edge-preserving ability of the algorithm. Secondly, the dark channel prior is added to suppress artifacts and stair effects. Finally, the weights of the prior terms are iteratively adapted to obtain the final high-resolution image. Experiments show that the proposed algorithm can achieve a better result in objective and subjective visual evaluations.
Keywords:
TV
Mathematical models
Image reconstruction
Image edge detection
Superresolution
Stairs
Frequency-domain analysis
Image process
multiframe super-resolution
dark channel prior
total variation prior
image prior combination
Bayesian framework
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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