737 research outputs found
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
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
An ELU Network with Total Variation for Image Denoising
In this paper, we propose a novel convolutional neural network (CNN) for
image denoising, which uses exponential linear unit (ELU) as the activation
function. We investigate the suitability by analyzing ELU's connection with
trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On
the other hand, batch normalization (BN) is indispensable for residual
denoising and convergence purpose. However, direct stacking of BN and ELU
degrades the performance of CNN. To mitigate this issue, we design an
innovative combination of activation layer and normalization layer to exploit
and leverage the ELU network, and discuss the corresponding rationale.
Moreover, inspired by the fact that minimizing total variation (TV) can be
applied to image denoising, we propose a TV regularized L2 loss to evaluate the
training effect during the iterations. Finally, we conduct extensive
experiments, showing that our model outperforms some recent and popular
approaches on Gaussian denoising with specific or randomized noise levels for
both gray and color images.Comment: 10 pages, Accepted by the 24th International Conference on Neural
Information Processing (2017
Dilated Deep Residual Network for Image Denoising
Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201
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