192 research outputs found
Learning Deep CNN Denoiser Prior for Image Restoration
Model-based optimization methods and discriminative learning methods have
been the two dominant strategies for solving various inverse problems in
low-level vision. Typically, those two kinds of methods have their respective
merits and drawbacks, e.g., model-based optimization methods are flexible for
handling different inverse problems but are usually time-consuming with
sophisticated priors for the purpose of good performance; in the meanwhile,
discriminative learning methods have fast testing speed but their application
range is greatly restricted by the specialized task. Recent works have revealed
that, with the aid of variable splitting techniques, denoiser prior can be
plugged in as a modular part of model-based optimization methods to solve other
inverse problems (e.g., deblurring). Such an integration induces considerable
advantage when the denoiser is obtained via discriminative learning. However,
the study of integration with fast discriminative denoiser prior is still
lacking. To this end, this paper aims to train a set of fast and effective CNN
(convolutional neural network) denoisers and integrate them into model-based
optimization method to solve other inverse problems. Experimental results
demonstrate that the learned set of denoisers not only achieve promising
Gaussian denoising results but also can be used as prior to deliver good
performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring
Image motion blur usually results from moving objects or camera shakes. Such
blur is generally directional and non-uniform. Previous research efforts
attempt to solve non-uniform blur by using self-recurrent multi-scale or
multi-patch architectures accompanying with self-attention. However, using
self-recurrent frameworks typically leads to a longer inference time, while
inter-pixel or inter-channel self-attention may cause excessive memory usage.
This paper proposes blur-aware attention networks (BANet) that accomplish
accurate and efficient deblurring via a single forward pass. Our BANet utilizes
region-based self-attention with multi-kernel strip pooling to disentangle blur
patterns of different degrees and with cascaded parallel dilated convolution to
aggregate multi-scale content features. Extensive experimental results on the
GoPro and HIDE benchmarks demonstrate that the proposed BANet performs
favorably against the state-of-the-art in blurred image restoration and can
provide deblurred results in real-time
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