# Joint Enhancement and Denoising Method via Sequential Decomposition

arXiv: https://arxiv.org/abs/1804.08468 · HTML: https://mlsanity.com/paper/1804.08468/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 30 of 32)

- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model](https://mlsanity.com/paper/2308.13164/) (2023)
- [R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network](https://mlsanity.com/paper/2106.14501/) (2021)
- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [CLE Diffusion: Controllable Light Enhancement Diffusion Model](https://mlsanity.com/paper/2308.06725/) (2023)
- [Towards Low Light Enhancement with RAW Images](https://mlsanity.com/paper/2112.14022/) (2021)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)
- [Self-supervised Low Light Image Enhancement and Denoising](https://mlsanity.com/paper/2103.00832/) (2021)
- [Debiased Subjective Assessment of Real-World Image Enhancement](https://mlsanity.com/paper/2106.10080/) (2021)
- [Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement](https://mlsanity.com/paper/2302.11464/) (2023)
- [DarkVision: A Benchmark for Low-light Image/Video Perception](https://mlsanity.com/paper/2301.06269/) (2023)
- [DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement](https://mlsanity.com/paper/2404.03327/) (2024)
- [DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.01809/) (2021)
- [Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement](https://mlsanity.com/paper/2207.00965/) (2022)
- [ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement](https://mlsanity.com/paper/2503.02484/) (2025)
- [Decoupled Low-light Image Enhancement](https://mlsanity.com/paper/2111.14458/) (2021)
- [Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2308.08197/) (2023)
- [TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.02477/) (2021)
- [The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement](https://mlsanity.com/paper/2202.09738/) (2022)
- [Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer](https://mlsanity.com/paper/2307.02625/) (2023)
- [CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement](https://mlsanity.com/paper/2404.05253/) (2024)
- [RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement](https://mlsanity.com/paper/2409.04363/) (2024)
- [Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model](https://mlsanity.com/paper/2202.02277/) (2022)
- [A ground-based dataset and a diffusion model for on-orbit low-light image enhancement](https://mlsanity.com/paper/2306.14227/) (2023)
- [Low-Light Enhancement in the Frequency Domain](https://mlsanity.com/paper/2306.16782/) (2023)
- [Low-Light Enhancement Effect on Classification and Detection: An Empirical Study](https://mlsanity.com/paper/2409.14461/) (2024)
- [BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration](https://mlsanity.com/paper/2106.15953/) (2021)
- [Attention based Broadly Self-guided Network for Low light Image Enhancement](https://mlsanity.com/paper/2112.06226/) (2021)
- [Joint Correcting and Refinement for Balanced Low-Light Image Enhancement](https://mlsanity.com/paper/2309.16128/) (2023)

## Abstract (arXiv)

Many low-light enhancement methods ignore intensive noise in original images. As a result, they often simultaneously enhance the noise as well. Furthermore, extra denoising procedures adopted by most methods ruin the details. In this paper, we introduce a joint low-light enhancement and denoising strategy, aimed at obtaining well-enhanced low-light images while getting rid of the inherent noise issue simultaneously. The proposed method performs Retinex model based decomposition in a successive sequence, which sequentially estimates a piece-wise smoothed illumination and a noise-suppressed reflectance. After getting the illumination and reflectance map, we adjust the illumination layer and generate our enhancement result. In this noise-suppressed sequential decomposition process we enforce the spatial smoothness on each component and skillfully make use of weight matrices to suppress the noise and improve the contrast. Results of extensive experiments demonstrate the effectiveness and practicability of our method. It performs well for a wide variety of images, and achieves better or comparable quality compared with the state-of-the-art methods.