# AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images

arXiv: https://arxiv.org/abs/2005.09305 · HTML: https://mlsanity.com/paper/2005.09305/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 29 of 29)

- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging](https://mlsanity.com/paper/2106.15944/) (2021)
- [Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues](https://mlsanity.com/paper/2312.11119/) (2023)
- [Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images](https://mlsanity.com/paper/2108.05547/) (2021)
- [Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild](https://mlsanity.com/paper/2108.06659/) (2021)
- [Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysis](https://mlsanity.com/paper/2401.03835/) (2024)
- [Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection](https://mlsanity.com/paper/2204.03929/) (2022)
- [A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging](https://mlsanity.com/paper/2407.19282/) (2024)
- [HADAR-Based Thermal Infrared Hyperspectral Image Restoration](https://mlsanity.com/paper/2605.13664/) (2026)
- [Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation](https://mlsanity.com/paper/2112.13003/) (2021)
- [HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution](https://mlsanity.com/paper/2112.14608/) (2021)
- [Deep-learning-based Hyperspectral imaging through a RGB camera](https://mlsanity.com/paper/2107.05190/) (2021)
- [SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution](https://mlsanity.com/paper/2310.00413/) (2023)
- [Programmable Spectral Filter Arrays using Phase Spatial Light Modulator](https://mlsanity.com/paper/2109.14450/) (2021)
- [Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading](https://mlsanity.com/paper/2410.05762/) (2024)
- [FRN: Fractal-Based Recursive Spectral Reconstruction Network](https://mlsanity.com/paper/2505.15439/) (2025)
- [HYDRA: HYbrid knowledge Distillation and spectral Reconstruction Algorithm for high channel hyperspectral camera applications](https://mlsanity.com/paper/2510.16664/) (2025)
- [A Weakly-Supervised Depth Estimation Network Using Attention Mechanism](https://mlsanity.com/paper/2107.04819/) (2021)
- [Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence](https://mlsanity.com/paper/2312.12833/) (2023)
- [Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images](https://mlsanity.com/paper/2501.01481/) (2025)
- [From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2503.06852/) (2025)
- [NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment](https://mlsanity.com/paper/2503.07004/) (2025)
- [Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution](https://mlsanity.com/paper/2505.24605/) (2025)
- [Modulate and Reconstruct: Learning Hyperspectral Imaging from Misaligned Smartphone Views](https://mlsanity.com/paper/2507.01835/) (2025)
- [A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging](https://mlsanity.com/paper/2507.04622/) (2025)
- [RGB Pre-Training Enhanced Unobservable Feature Latent Diffusion Model for Spectral Reconstruction](https://mlsanity.com/paper/2507.12967/) (2025)
- [USCTNet: A deep unfolding nuclear-norm optimization solver for physically consistent HSI reconstruction](https://mlsanity.com/paper/2509.10651/) (2025)
- [Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function](https://mlsanity.com/paper/2606.05759/) (2026)
- [Spectral Super-Resolution using Spatial-Spectral Residual Operator Networks](https://mlsanity.com/paper/2609.35410/) (2026)

## Abstract (arXiv)

Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs). Nevertheless, most CNN-based SR algorithms neglect to explore the camera spectral sensitivity (CSS) prior and interdependencies among intermediate features, thus limiting the representation ability of the network and performance of SR. To conquer these issues, we propose a novel adaptive weighted attention network (AWAN) for SR, whose backbone is stacked with multiple dual residual attention blocks (DRAB) decorating with long and short skip connections to form the dual residual learning. Concretely, we investigate an adaptive weighted channel attention (AWCA) module to reallocate channel-wise feature responses via integrating correlations between channels. Furthermore, a patch-level second-order non-local (PSNL) module is developed to capture long-range spatial contextual information by second-order non-local operations for more powerful feature representations. Based on the fact that the recovered RGB images can be projected by the reconstructed hyperspectral image (HSI) and the given CSS function, we incorporate the discrepancies of the RGB images and HSIs as a finer constraint for more accurate reconstruction. Experimental results demonstrate the effectiveness of our proposed AWAN network in terms of quantitative comparison and perceptual quality over other state-of-the-art SR methods. In the NTIRE 2020 Spectral Reconstruction Challenge, our entries obtain the 1st ranking on the Clean track and the 3rd place on the Real World track. Codes are available at https://github.com/Deep-imagelab/AWAN.