# Color Shift Estimation-and-Correction for Image Enhancement

arXiv: https://arxiv.org/abs/2405.17725 · HTML: https://mlsanity.com/paper/2405.17725/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [TDAN: Temporally Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/1812.02898/) (2018)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [Low-Light Image Enhancement via Structure Modeling and Guidance](https://mlsanity.com/paper/2305.05839/) (2023)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [Object Detection in Video with Spatiotemporal Sampling Networks](https://mlsanity.com/paper/1803.05549/) (2018)
- [Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2304.07039/) (2023)
- [Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.00984/) (2021)
- [Abandoning the Bayer-Filter to See in the Dark](https://mlsanity.com/paper/2203.04042/) (2022)
- [Lighting up NeRF via Unsupervised Decomposition and Enhancement](https://mlsanity.com/paper/2307.10664/) (2023)
- [Image Correction via Deep Reciprocating HDR Transformation](https://mlsanity.com/paper/1804.04371/) (2018)
- [Dual Illumination Estimation for Robust Exposure Correction](https://mlsanity.com/paper/1910.13688/) (2019)
- [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022)
- [Unifying Nonlocal Blocks for Neural Networks](https://mlsanity.com/paper/2108.02451/) (2021)

## Built on by (most cited first, 14 of 14)

- [LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes](https://mlsanity.com/paper/2411.06757/) (2024)
- [See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction](https://mlsanity.com/paper/2505.20641/) (2025)
- [FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching](https://mlsanity.com/paper/2509.23608/) (2025)
- [Endo-4DGX: Robust Endoscopic Scene Reconstruction and Illumination Correction with Gaussian Splatting](https://mlsanity.com/paper/2506.23308/) (2025)
- [Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset](https://mlsanity.com/paper/2510.17585/) (2025)
- [Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation](https://mlsanity.com/paper/2412.00277/) (2024)
- [LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement](https://mlsanity.com/paper/2502.15186/) (2025)
- [Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding](https://mlsanity.com/paper/2505.06991/) (2025)
- [UNICE: Training A Universal Image Contrast Enhancer](https://mlsanity.com/paper/2507.17157/) (2025)
- [Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery](https://mlsanity.com/paper/2507.23150/) (2025)
- [LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction](https://mlsanity.com/paper/2511.06066/) (2025)
- [CLIP-Guided Unsupervised Semantic-Aware Exposure Correction](https://mlsanity.com/paper/2601.19129/) (2026)
- [Rethinking Exposure Correction for Spatially Non-uniform Degradation](https://mlsanity.com/paper/2604.04136/) (2026)
- [LoopLUT: 3D Lookup Tables with Progressive Region Refinement for Real-Time 4K Image Enhancement](https://mlsanity.com/paper/2609.33593/) (2026)

## Abstract (arXiv)

Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate the color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and under-exposed regions display opposite color tone distribution shifts with respect to each other, which may not be easily normalized in joint modeling as they usually do not have ``normal-exposed'' regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project webpage: https://github.com/yiyulics/CSEC.