# Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues

arXiv: https://arxiv.org/abs/2107.09237 · HTML: https://mlsanity.com/paper/2107.09237/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks](https://mlsanity.com/paper/1810.12348/) (2018)
- [Strip Pooling: Rethinking Spatial Pooling for Scene Parsing](https://mlsanity.com/paper/2003.13328/) (2020)
- [Infrared Colorization Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.02245/) (2016)

## Built on by (most cited first, 3 of 3)

- [Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues](https://mlsanity.com/paper/2312.11119/) (2023)
- [Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation](https://mlsanity.com/paper/2312.16040/) (2023)
- [Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization](https://mlsanity.com/paper/2601.01103/) (2026)

## Abstract (arXiv)

Near infrared (NIR) imaging has been widely applied in low-light imaging scenarios; however, it is difficult for human and algorithms to perceive the real scene in the colorless NIR domain. While Generative Adversarial Network (GAN) has been widely employed in various image colorization tasks, it is challenging for a direct mapping mechanism, such as a conventional GAN, to transform an image from the NIR to the RGB domain with correct semantic reasoning, well-preserved textures, and vivid color combinations concurrently. In this work, we propose a novel Attention-based NIR image colorization framework via Adaptive Fusion of Semantic and Texture clues, aiming at achieving these goals within the same framework. The tasks of texture transfer and semantic reasoning are carried out in two separate network blocks. Specifically, the Texture Transfer Block (TTB) aims at extracting texture features from the NIR image's Laplacian component and transferring them for subsequent color fusion. The Semantic Reasoning Block (SRB) extracts semantic clues and maps the NIR pixel values to the RGB domain. Finally, a Fusion Attention Block (FAB) is proposed to adaptively fuse the features from the two branches and generate an optimized colorization result. In order to enhance the network's learning capacity in semantic reasoning as well as mapping precision in texture transfer, we have proposed the Residual Coordinate Attention Block (RCAB), which incorporates coordinate attention into a residual learning framework, enabling the network to capture long-range dependencies along the channel direction and meanwhile precise positional information can be preserved along spatial directions. RCAB is also incorporated into FAB to facilitate accurate texture alignment during fusion. Both quantitative and qualitative evaluations show that the proposed method outperforms state-of-the-art NIR image colorization methods.