Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image non-local self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI

Distributed multi-vehicle task assignment in a time-invariant drift field with obstacles

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal

San Bruno, puerta a los cerros: arquitectura como vínculo entre el ciudadano y su entorno natural

Artículo de gradoSe realiza un proyecto urbano a escala de tres barrios: Egipto, El Parejo y La Peña. igualmente se realiza un proyecto urbano a menor escala en el sector San Bruno (Egipto) y un proyecto arquitectónico dentro de este, en la entrada a los Cerros Orientales de Bogotá, se propone una casa del árbol.1. INTRODUCCIÓN 1.1 DISPOSITIVOS DE APROPIACIÓN DEMOCRATICA 2. METODOLOGÍA 3. RESULTADOS 3.1 ETAPAS DE DESARROLLO 3.2 BARRIO EGIPTO, EL PAREJO Y LA PEÑA 3.3 SECTOR SAN BRUNO 3.4 MEMORIA Y ACCESIBILIDAD 3.5 BOSQUE DE COLUMNAS 3.5.1 ACTIVA 3.5.2 PASIVA 3.5.3 PRODUCTIVA 4. LA CASA DEL ARBOL 5. DISCUSIÓN 6. CONCLUSION 7. REFERENCIAS 8. ANEXOSPregradoArquitect