KAUST DepartmentVisual Computing Center, King Abdullah University of Science and Technology (KAUST)
Online Publication Date2017-12-25
Print Publication Date2017-10
Permanent link to this recordhttp://hdl.handle.net/10754/626836
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AbstractConvolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
CitationChoudhury, B., Swanson, R., Heide, F., Wetzstein, G., & Heidrich, W. (2017). Consensus Convolutional Sparse Coding. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.459
SponsorsVCC KAUST Baseline Funding, Terman Faculty Fellowship, Intel Compressive Sensing Alliance, National Science Foundation (IIS 1553333), and NSF/Intel Partnership on Visual and Experiential Computing (IIS 1539120).
Conference/Event nameInternational Conference on Computer Vision (ICCV)