Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

In this paper, we propose a residual non-local attention network for high-quality image restoration.
Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features.
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block.
The trunk branch is used to extract hierarchical features.
Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions.
The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map.
Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network.
Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.
Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
Image restoration aims to recover high-quality (HQ) images from their corrupted low-quality (LQ) observations and plays a fundamental role in various high-level vision tasks.
It is a typical ill-posed problem due to the irreversible nature of the image degradation process.
Some most widely studied image restoration tasks include image denoising, demosaicing, and compression artifacts reduction.
By distinctively modelling the restoration process from LQ observations to HQ objectives, i.e., without assumption for a specific restoration task when modelling, these tasks can be uniformly addressed in the same framework.
Recently, deep convolutional neural network (CNN) has shown extraordinary capability of modelling various vision problems, ranging from low-level (e.g., image denoising (Zhang et al., 2017a) , compression artifacts reduction BID10 , and image super-resolution BID43 BID37 BID40 ) to high-level (e.g., image recognition ) vision applications.However, there are mainly three issues in the existing CNN based methods above.
First, the receptive field size of these networks is relatively small.
Most of them extract features in a local way with convolutional operation, which fails to capture the long-range dependencies between pixels in the whole image.
A larger receptive field size allows to make better use of training inputs and more context information.
This would be very helpful to capture the latent degradation model of LQ images, especially when the images suffer from heavy corruptions.
Second, distinctive ability of these networks is also limited.
Let's take image denoising as an example.
For a noisy image, the noise may appear in both the plain and textural regions.
Noise removal would be easier in the plain area than that in the textural one.
It is desired to make the denoising model focus on textual area more.
However, most previous denoising methods neglect to consider different contents in the noisy input and treat them equally.
This would result in over-smoothed outputs and some textural details would also fail to be recovered.
Third, all channel-wise features are treated equally in those networks.
This naive treatment lacks flexibility in dealing with different types of information (e.g., low-and high-frequency information).
For a set of features, some contain more information related to HQ image and the others may contain more information related to corruptions.
The interdependencies among channels should be considered for more accurate image restoration.To address the above issues, we propose the very deep residual non-local attention networks (RNAN) for high-quality image restoration.
We design residual local and non-local attention blocks as the basic building modules for the very deep network.
Each attention block consists of trunk and mask branches.
We introduce residual block for trunk branch and extract hierarchical features.
For mask branch, we conduct feature downscaling and upscaling with largestride convolution and deconvolution to enlarge receptive field size.
Furthermore, we incorporate non-local block in the mask branch to obtain residual non-local mixed attention.
We apply RNAN for various restoration tasks, including image denoising, demosaicing, and compression artifacts reduction.
Extensive experiments show that our proposed RNAN achieves state-of-the-art results compared with other recent leading methods in all tasks.
To the best of our knowledge, this is the first time to consider residual non-local attention for image restoration problems.The main contributions of this work are three-fold:• We propose the very deep residual non-local networks for high-quality image restoration.The powerful networks are based on our proposed residual local and non-local attention blocks, which consist of trunk and mask branches.
The network obtains non-local mixed attention with non-local block in the mask branch.
Such attention mechanis helps to learn local and non-local information from the hierarchical features.•
We propose residual non-local attention learning to train very deep networks by preserving more low-level features, being more suitable for image restoration. Using
non-local lowlevel and high-level attention from the very deep network, we can pursue better network representational ability and finally obtain high-quality image restoration results.• We demonstrate
with extensive experiments that our RNAN is powerful for various image restoration tasks. RNAN achieves superior
results over leading methods for image denoising, demosaicing, compression artifacts reduction, and super-resolution. In addition, RNAN achieves
superior performance with moderate model size and performs very fast.
New state-of-the-art framework for image restoration