New decomposition method enhances image restoration with cone constraints

Researchers have developed a new method for image restoration using a cone-constrained bilinear decomposition of the total scaled-gradient variation (TSGV) regularizer. This approach addresses the computational challenges posed by the TSGV regularizer's nonconvex nature, offering an equivalent optimization problem with cone or sphere constraints. The method, solved via alternating minimization, demonstrates monotonic energy decrease and global convergence. Experiments in Gaussian denoising and non-line-of-sight imaging show competitive or superior performance in peak signal-to-noise ratio and Structural Similarity Index Measure compared to existing variational methods, particularly at high noise levels. AI

影响 This research could lead to more robust and efficient image restoration techniques in computer vision applications.

排序理由 The item is an academic paper detailing a new computational method for image processing. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gaussian denoising
• Gotit.pub
• Hugging Face
• Influence Flower
• non-line-of-sight (NLOS) imaging
• peak signal-to-noise ratio
• ScienceCast
• Structural Similarity Index Measure
• Total Scaled-Gradient Variation

• 论文
• 其他

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