31 research outputs found
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
In this paper, we propose a very deep fully convolutional encoding-decoding
framework for image restoration such as denoising and super-resolution. The
network is composed of multiple layers of convolution and de-convolution
operators, learning end-to-end mappings from corrupted images to the original
ones. The convolutional layers act as the feature extractor, which capture the
abstraction of image contents while eliminating noises/corruptions.
De-convolutional layers are then used to recover the image details. We propose
to symmetrically link convolutional and de-convolutional layers with skip-layer
connections, with which the training converges much faster and attains a
higher-quality local optimum. First, The skip connections allow the signal to
be back-propagated to bottom layers directly, and thus tackles the problem of
gradient vanishing, making training deep networks easier and achieving
restoration performance gains consequently. Second, these skip connections pass
image details from convolutional layers to de-convolutional layers, which is
beneficial in recovering the original image. Significantly, with the large
capacity, we can handle different levels of noises using a single model.
Experimental results show that our network achieves better performance than all
previously reported state-of-the-art methods.Comment: Accepted to Proc. Advances in Neural Information Processing Systems
(NIPS'16). Content of the final version may be slightly different. Extended
version is available at http://arxiv.org/abs/1606.0892
A Study on Clustering for Clustering Based Image De-Noising
In this paper, the problem of de-noising of an image contaminated with
Additive White Gaussian Noise (AWGN) is studied. This subject is an open
problem in signal processing for more than 50 years. Local methods suggested in
recent years, have obtained better results than global methods. However by more
intelligent training in such a way that first, important data is more effective
for training, second, clustering in such way that training blocks lie in
low-rank subspaces, we can design a dictionary applicable for image de-noising
and obtain results near the state of the art local methods. In the present
paper, we suggest a method based on global clustering of image constructing
blocks. As the type of clustering plays an important role in clustering-based
de-noising methods, we address two questions about the clustering. The first,
which parts of the data should be considered for clustering? and the second,
what data clustering method is suitable for de-noising.? Then clustering is
exploited to learn an over complete dictionary. By obtaining sparse
decomposition of the noisy image blocks in terms of the dictionary atoms, the
de-noised version is achieved. In addition to our framework, 7 popular
dictionary learning methods are simulated and compared. The results are
compared based on two major factors: (1) de-noising performance and (2)
execution time. Experimental results show that our dictionary learning
framework outperforms its competitors in terms of both factors.Comment: 9 pages, 8 figures, Journal of Information Systems and
Telecommunications (JIST
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Learning Clustering-Based Linear Mappings for Quantization Noise Removal
International audienceThis paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted to the decoder where they can be applied to perform de-noising. The method has been tested with the HEVC standard, leading to a bitrate saving of up to 9.63%
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