Open AccessPosted Content
Residual Non-local Attention Networks for Image Restoration.
Reads0
Chats0
TLDR
The proposed residual local and non-local attention learning to train the very deep network is generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.Abstract:
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.read more
Citations
More filters
Posted Content
Pre-Trained Image Processing Transformer
Hanting Chen,Yunhe Wang,Tianyu Guo,Chang Xu,Yiping Deng,Zhenhua Liu,Siwei Ma,Chunjing Xu,Chao Xu,Wen Gao +9 more
TL;DR: To maximally excavate the capability of transformer, the IPT model is presented to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs and the contrastive learning is introduced for well adapting to different image processing tasks.
Proceedings ArticleDOI
Lightweight Image Super-Resolution with Information Multi-distillation Network
TL;DR: Zheng et al. as mentioned in this paper proposed a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multidistillation blocks (IMDB), which contains distillation and selective fusion parts.
Book ChapterDOI
Learning Enriched Features for Real Image Restoration and Enhancement
Syed Waqas Zamir,Aditya Arora,Salman Khan,Munawar Hayat,Fahad Shahbaz Khan,Ming-Hsuan Yang,Ling Shao +6 more
TL;DR: MIRNet as mentioned in this paper proposes a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting mult-scale features, (b) information exchange across the multiresolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention-based multiscale feature aggregation.
Book ChapterDOI
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification
TL;DR: An intra-modality weighted-part attention module to extract discriminative part-aggregated features, by imposing the domain knowledge on the part relationship mining, and a parameter-free dynamic dual aggregation learning strategy to adaptively integrate the two components in a progressive joint training manner.
Proceedings ArticleDOI
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution
TL;DR: A novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks, which is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.