Fast Multiclass Dictionaries Learning with Geometrical Directions in Sparse Image Reconstruction

欠采样磁共振成像方法通过减少采集数据量来加速成像，并利用图像重建方法得到完整的磁共振图像。这类方法在抑制心脏和腹部等成像运动伪影上具有良好的应用前景，其中利用图像稀疏性的压缩感知方法是磁共振成像的研究热点之一。在图像稀疏重建中，图像稀疏表示的前向逼近误差是图像重建反问题中的重建误差的上限。因此，如何设计稀疏变换来降低图像表示误差进而提高重建图像质量有着重要意义。诸如小波变换只能普适地表示各种图像，而对某一特定重建目标图像的稀疏表示能力有限。因此，近几年学者重点关注图像的自适应稀疏表示，并发现自适应稀疏变换重建的图像质量明显优于典型的非自适应稀疏变换。但是，诸如K-SVD等自适应训练图像表示的方...Compressed sensing magnetic resonance imaging has shown great capability to accelerate data acquisition by exploiting sparsity of images under a certain transform or dictionary. Sparser representations usually lead to lower reconstruction errors, thus enduring efforts have been made to find dictionaries that provide sparser representation of magnetic resonance images. Previously, adaptive sparse r...学位：工程硕士院系专业：物理科学与技术学院_工程硕士(电子与通信工程)学号：3332014115283

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1