Image domain dual material decomposition for dual‐energyCTusing butterfly network

Hamilton Cycle Decomposition of the Butterfly Network

in this paper, we prove that the wrapped Butterfly graph WBF(d; n) of degree d and dimension n is decomposable into Hamilton cycles. This answers a conjecture of D. Barth and A. Raspaud who solved the case d = 2. Key-words: Butterfly graph, graph theory, Hamiltonism, Hamilton decomposition, Hamilton cycle, Hamilton circuit, perfect matching. (Résumé : tsvp) This work has been supported by the C...

Sample-oriented Domain Adaptation for Image Classification

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...

Variational domain decomposition for parallel image processing

Bregmanized Domain Decomposition for Image Restoration

Computational problems of large-scale appearing in biomedical imaging, astronomy, art restoration, and data analysis are gaining recently a lot of attention due to better hardware, higher dimensionality of images and data sets, more parameters to be measured, and an increasing number of data acquired. In the last couple of years non-smooth minimization problems such as total variation minimizat...

Domain decomposition method for image deblurring

As a fundamental problem in image processing, image deblurring has still attracted a lot of research attention. Due to the large computational cost, especially for high-resolution images, it becomes challenging to solve the deblurring minimization problem and the underlying partial differential equations. The domain decomposition method (DD), as one of themost efficient algorithms for solving l...