研究目的
Investigating the influence of edge selection on blurring kernel estimation and proposing an image deblurring algorithm based on edge selection to estimate blurring kernel and latent clear image simultaneously under the framework of maximum a posterior probability estimation.
研究成果
The proposed image deblurring algorithm based on edge selection can estimate the optimal blurring kernel and restore the latent clear image with better visual experience under the framework of MAP.
研究不足
The paper does not explicitly mention the limitations of the research.
1：Experimental Design and Method Selection:
The paper proposes an image deblurring algorithm based on edge selection to estimate blurring kernel and latent clear image simultaneously under the framework of MAP.
2：Sample Selection and Data Sources:
Synthetic blurring images are used for experimentation.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The algorithm involves edge selection, blurring kernel estimation, and image restoration steps.
5：Data Analysis Methods:
The effectiveness of the algorithm is verified through comparative experiments with classical blind deconvolution algorithms, using PSNR for evaluation.
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