论文标题
图像模型和先验的恢复问题的关系的固定理论研究
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems
论文作者
论文摘要
图像先验建模是图像恢复，计算成像，压缩传感和其他反问题的关键问题。最近结合了多个有效先验的算法，例如稀疏或低级别模型，在各种应用中都表现出了出色的性能。但是，流行图像模型之间的关系尚不清楚，并且一般没有理论来证明其联系。在本文中，我们对图像模型进行了理论分析，以弥合应用和图像事先理解之间的差距，包括稀疏性，群体的稀疏性，关节稀疏性和低率性等。我们系统地研究了每个图像模型的有效图像恢复的有效性。此外，我们通过将多个模型和图像模型关系结合起来将降解性能改进联系起来。进行了广泛的实验，以比较与我们的分析一致的脱索结果。除了基于模型的方法之外，我们定量地证明了图像属性是通过深度学习方法毫无疑问的图像属性，通过深度学习方法可以通过与其互补图像模型结合来进一步提高DeNoSis的性能。
Image prior modeling is the key issue in image recovery, computational imaging, compresses sensing, and other inverse problems. Recent algorithms combining multiple effective priors such as the sparse or low-rank models, have demonstrated superior performance in various applications. However, the relationships among the popular image models are unclear, and no theory in general is available to demonstrate their connections. In this paper, we present a theoretical analysis on the image models, to bridge the gap between applications and image prior understanding, including sparsity, group-wise sparsity, joint sparsity, and low-rankness, etc. We systematically study how effective each image model is for image restoration. Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships. Extensive experiments are conducted to compare the denoising results which are consistent with our analysis. On top of the model-based methods, we quantitatively demonstrate the image properties that are inexplicitly exploited by deep learning method, of which can further boost the denoising performance by combining with its complementary image models.
