锐化
核(代数)
全色胶片
计算机科学
图像分辨率
人工智能
计算机视觉
拉普拉斯算子
先验概率
图像(数学)
超分辨率
最优化问题
算法
模式识别(心理学)
数学
组合数学
数学分析
贝叶斯概率
作者
Lantao Yu,Dehong Liu,Hassan Mansour,Petros T. Boufounos,Yanting Ma
标识
DOI:10.1109/icassp40776.2020.9053554
摘要
We address the problem of sharpening low spatial-resolution multi-spectral (MS) images with their associated misaligned high spatial-resolution panchromatic (PAN) image, based on priors on the spatial blur kernel and on the cross-channel relationship. In particular, we formulate the blind pan-sharpening problem within a multi-convex optimization framework using total generalized variation for the blur kernel and local Laplacian prior for the cross-channel relationship. The problem is solved by the alternating direction method of multipliers (ADMM), which alternately updates the blur kernel and sharpens intermediate MS images. Numerical experiments demonstrate that our approach is more robust to large misalignment errors and yields better super resolved MS images compared to state-of-the-art optimization-based and deep-learning-based algorithms.
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