How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods

Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures

Through Changing Scenes: Architecture and Community Values in Little Rock's Historic Churches

This paper investigates why and how six historic urban churches in Little Rock, Arkansas adapted architecturally to changing community needs. In approaching this research, the researcher examined a wide variety of information: what events motivated building alterations, how the community and congregation viewed the church structure, and how churches utilized their buildings to house community services. The churches selected for this study are located within the original nineteenth century city boundary. The social and cultural landscape of the city have changed dramatically over the last century with the urbanization and reform of the Progressive Era, the social unrest and rise of fundamentalism during the War Years, racial tension and urban renewal efforts of the 1950s through the 1970s, and downtown revitalization and preservation concerns of the present era. The researcher compiled Primary source documents to discern each congregation's growth pattern within each era, then analyzed the churches in each time periods in Little Rock's history for a variety of architectural and social themes. The trends that emerged resulted in typologies of church growth. Churches followed similar trends architecturally with regards to style, building materials, and furnishings, as well as patterns in building use. This investigation seeks to look at the churches holistically, not simply as significant architectural structures, but also as community hubs, housing critical spaces that shaped Little Rock's urban community

Prototypes, Location, and Associative Networks (PLAN): Towards a Unified Theory of Cognitive Mapping

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98111/1/s15516709cog1901_1.pd