18,039 research outputs found
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
Learning a Mixture of Deep Networks for Single Image Super-Resolution
Single image super-resolution (SR) is an ill-posed problem which aims to
recover high-resolution (HR) images from their low-resolution (LR)
observations. The crux of this problem lies in learning the complex mapping
between low-resolution patches and the corresponding high-resolution patches.
Prior arts have used either a mixture of simple regression models or a single
non-linear neural network for this propose. This paper proposes the method of
learning a mixture of SR inference modules in a unified framework to tackle
this problem. Specifically, a number of SR inference modules specialized in
different image local patterns are first independently applied on the LR image
to obtain various HR estimates, and the resultant HR estimates are adaptively
aggregated to form the final HR image. By selecting neural networks as the SR
inference module, the whole procedure can be incorporated into a unified
network and be optimized jointly. Extensive experiments are conducted to
investigate the relation between restoration performance and different network
architectures. Compared with other current image SR approaches, our proposed
method achieves state-of-the-arts restoration results on a wide range of images
consistently while allowing more flexible design choices. The source codes are
available in http://www.ifp.illinois.edu/~dingliu2/accv2016
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