3 research outputs found
Proximal Splitting Networks for Image Restoration
Image restoration problems are typically ill-posed requiring the design of
suitable priors. These priors are typically hand-designed and are fully
instantiated throughout the process. In this paper, we introduce a novel
framework for handling inverse problems related to image restoration based on
elements from the half quadratic splitting method and proximal operators.
Modeling the proximal operator as a convolutional network, we defined an
implicit prior on the image space as a function class during training. This is
in contrast to the common practice in literature of having the prior to be
fixed and fully instantiated even during training stages. Further, we allow
this proximal operator to be tuned differently for each iteration which greatly
increases modeling capacity and allows us to reduce the number of iterations by
an order of magnitude as compared to other approaches. Our final network is an
end-to-end one whose run time matches the previous fastest algorithms while
outperforming them in recovery fidelity on two image restoration tasks. Indeed,
we find our approach achieves state-of-the-art results on benchmarks in image
denoising and image super resolution while recovering more complex and finer
details
Designing and Training of A Dual CNN for Image Denoising
Deep convolutional neural networks (CNNs) for image denoising have recently
attracted increasing research interest. However, plain networks cannot recover
fine details for a complex task, such as real noisy images. In this paper, we
propsoed a Dual denoising Network (DudeNet) to recover a clean image.
Specifically, DudeNet consists of four modules: a feature extraction block, an
enhancement block, a compression block, and a reconstruction block. The feature
extraction block with a sparse machanism extracts global and local features via
two sub-networks. The enhancement block gathers and fuses the global and local
features to provide complementary information for the latter network. The
compression block refines the extracted information and compresses the network.
Finally, the reconstruction block is utilized to reconstruct a denoised image.
The DudeNet has the following advantages: (1) The dual networks with a parse
mechanism can extract complementary features to enhance the generalized ability
of denoiser. (2) Fusing global and local features can extract salient features
to recover fine details for complex noisy images. (3) A Small-size filter is
used to reduce the complexity of denoiser. Extensive experiments demonstrate
the superiority of DudeNet over existing current state-of-the-art denoising
methods
Deep Learning on Image Denoising: An overview
Deep learning techniques have received much attention in the area of image
denoising. However, there are substantial differences in the various types of
deep learning methods dealing with image denoising. Specifically,
discriminative learning based on deep learning can ably address the issue of
Gaussian noise. Optimization models based on deep learning are effective in
estimating the real noise. However, there has thus far been little related
research to summarize the different deep learning techniques for image
denoising. In this paper, we offer a comparative study of deep techniques in
image denoising. We first classify the deep convolutional neural networks
(CNNs) for additive white noisy images; the deep CNNs for real noisy images;
the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images,
which represents the combination of noisy, blurred and low-resolution images.
Then, we analyze the motivations and principles of the different types of deep
learning methods. Next, we compare the state-of-the-art methods on public
denoising datasets in terms of quantitative and qualitative analysis. Finally,
we point out some potential challenges and directions of future research