Image restoration using sub-images and confidence intervals
In applications, images are recorded, blurred, and noisy. This work aims to compute confidence intervals for quantifying the uncertainty of a reconstructed image. By partitioning the image into sub-images, the algorithm remains effective while becoming more efficient. Sub-images also provide flexibility to focus on a local reconstruction, focusing on the parts of the image with the most importance. Many time-sensitive applications require signal restoration and would benefit from this reconstruction process.
Mathematics, Other Applied Mathematics, Theory and Algorithms
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