# COLA-Net: Collaborative Attention Network for Image Restoration arXiv: https://arxiv.org/abs/2103.05961 ยท HTML: https://mlsanity.com/paper/2103.05961/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 51 references that are in the map. - First posted on arXiv 10 Mar 2021. ## Builds on (most cited first, 30 of 31) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017) - [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017) - [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017) - [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019) - [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020) - [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018) - [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018) - [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017) - [Residual Non-local Attention Networks for Image Restoration](https://mlsanity.com/paper/1903.10082/) (2019) - [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020) - [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017) - [Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration](https://mlsanity.com/paper/1508.02848/) (2015) - [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019) - [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018) - [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015) - [Variational Denoising Network: Toward Blind Noise Modeling and Removal](https://mlsanity.com/paper/1908.11314/) (2019) - [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018) - [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020) - [A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising](https://mlsanity.com/paper/1807.04364/) (2018) - [Group-based Sparse Representation for Image Restoration](https://mlsanity.com/paper/1405.3351/) (2014) - [Non-Local Color Image Denoising with Convolutional Neural Networks](https://mlsanity.com/paper/1611.06757/) (2016) - [Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising](https://mlsanity.com/paper/1806.05229/) (2018) - [Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization](https://mlsanity.com/paper/1404.7566/) (2014) - [Learning Non-local Image Diffusion for Image Denoising](https://mlsanity.com/paper/1702.07472/) (2017) - [Dilated Deep Residual Network for Image Denoising](https://mlsanity.com/paper/1708.05473/) (2017) - [Image Restoration Using Joint Statistical Modeling in Space-Transform Domain](https://mlsanity.com/paper/1405.3173/) (2014) ## Built on by (most cited first, 16 of 16) - [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021) - [Deep Generalized Unfolding Networks for Image Restoration](https://mlsanity.com/paper/2204.13348/) (2022) - [Dynamic Attentive Graph Learning for Image Restoration](https://mlsanity.com/paper/2109.06620/) (2021) - [WaveDM: Wavelet-Based Diffusion Models for Image Restoration](https://mlsanity.com/paper/2305.13819/) (2023) - [Detail-Preserving Transformer for Light Field Image Super-Resolution](https://mlsanity.com/paper/2201.00346/) (2022) - [All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model](https://mlsanity.com/paper/2411.07445/) (2024) - [Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser](https://mlsanity.com/paper/2103.10234/) (2021) - [TransCL: Transformer Makes Strong and Flexible Compressive Learning](https://mlsanity.com/paper/2207.11972/) (2022) - [Memory augment is All You Need for image restoration](https://mlsanity.com/paper/2309.01377/) (2023) - [A cross Transformer for image denoising](https://mlsanity.com/paper/2310.10408/) (2023) - [CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising](https://mlsanity.com/paper/2211.14576/) (2022) - [Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI](https://mlsanity.com/paper/2209.13727/) (2022) - [Haze-Aware Attention Network for Single-Image Dehazing](https://mlsanity.com/paper/2407.11505/) (2024) - [Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets](https://mlsanity.com/paper/2206.04431/) (2022) - [NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing](https://mlsanity.com/paper/2305.03899/) (2023) - [Space-Time Attention with Shifted Non-Local Search](https://mlsanity.com/paper/2309.16849/) (2023) ## Abstract (arXiv) Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available on https://github.com/MC-E/COLA-Net.