Consensus Convolutional Sparse Coding

B. Choudhury, R. Swanson, F. Heide, G. Wetzstein, W. Heidrich
ICCV, (2017)

Consensus Convolutional Sparse Coding

Keywords

Convolutional sparse coding, High-level vision tasks, Low-level image reconstruction

Abstract

​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.

Code

https://github.com/vccimaging/CCSC_code_ICCV2017

Sources

Website PDF

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