Consensus Convolutional Sparse Coding

Biswarup Choudhury*, Robin Swanson*, Felix Heide*, Gordon Wetzstein, Wolfgang Heidrich
* Denotes Equal Contribution

Accepted to ICCV, 2017

Illustration of traditional CSC (left) and the proposed CCSC (right). CCSC lifts the prohibitive memory limitations of existing algorithms by breaking large, high dimensional datasets into tractable subproblems which can be efficiently solved with low memory footprint.(Computer Tower icon by Melvin is licensed under CC-BY 3.0.)


Convolutional 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 highdimensional 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.


Paper: [Bis2017CCSC.pdf (~4.1MB)] 
Supplementary: [Bis2017CCSC_supp.pdf (~17.4MB)] 
Code: Github (external link) 
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