Multinex is a lightweight model for low-light image enhancement. It is designed to brighten dark images, recover clearer colors, and improve visible detail while staying very small and efficient.

Multinex is built around a simple idea: instead of relying only on raw RGB input, it uses multiple useful image cues related to brightness and color, then combines them through a compact network. This helps it perform strongly even at a very small model size.

Model Description

Many low-light enhancement methods are either large and expensive to run, or they struggle to separate brightness changes from color correction cleanly.

Multinex addresses this with a lightweight and structured design. It uses:

a brightness-oriented branch
a color-oriented branch
compact fusion modules
carefully chosen analytic image priors

These priors give the model a stronger starting point, so it does not need to learn everything from scratch.

Why Multinex?

Multinex is useful when you want:

strong low-light enhancement
a small and efficient model
better brightness and color recovery
a method suitable for edge or real-time use
a practical preprocessing step for downstream vision tasks

Main advantages

Lightweight: very small compared to many recent methods
Efficient: suitable for resource-constrained settings
Structured: uses separate cues for brightness and color
Practical: useful both for visual enhancement and downstream tasks
Robust: performs well across both reference and no-reference benchmarks

How It Works

Multinex takes a dark RGB image and improves it using two types of guidance:

luminance guidance, which helps with brightness
reflectance guidance, which helps with color and appearance

These two forms of guidance are processed by a lightweight network and combined into the final enhanced image.

In simple terms, Multinex tries to answer two questions:

how should this image be brightened?
how should its colors and details be corrected?

By handling these separately, the model can enhance low-light images more effectively.

Prior Stacks

A key part of Multinex is its use of prior stacks.

Luminance guidance stack

This stack provides different views of image brightness. It helps the model understand where the image is dark, how light is distributed, and how brightness should be adjusted.

Reflectance guidance stack

This stack provides different views of image color and chromatic structure. It helps the model better recover color, preserve regions, and reduce unwanted artifacts.

Together, these stacks give the model a richer and more stable representation of the image.

Intended Uses

Direct use

Enhance dark photos
Improve brightness and color visibility
Recover clearer low-light images
Preprocess images before other vision tasks

Downstream use

Machine vision
Low-light object detection
Surveillance and nighttime imaging
Embedded and edge systems
Mobile photography enhancement
Robotics in dim environments

Training and Evaluation Data

Multinex is evaluated on common low-light image enhancement benchmarks, including:

LOL-v1
LOL-v2-real
LOL-v2-syn
MEF
LIME
DICM
NPE
ExDark for downstream detection evaluation

Please refer to the paper and code for exact training details and evaluation settings.

Performance

The paper reports that Multinex:

outperforms earlier lightweight and micro models in several settings
achieves strong no-reference perceptual quality
remains competitive with much larger methods
improves downstream object detection performance in low-light scenes

Example paired restoration results

Dataset	PSNR	SSIM	LPIPS
LOL-v1	23.19	0.843	0.129
LOL-v2-real	23.04	0.860	0.178
LOL-v2-syn	25.04	0.930	0.068

Example no-reference results

Metric	Mean
NIQE ↓	3.64
BRISQUE ↓	15.89

Efficiency

GFLOPs computed on 256x256x3 inputs.

Variant	Parameters	GFLOPs
Multinex	0.0447M	2.50
Multinex-Nano	0.0007M	0.04

Citation

If you use Multinex in your work, please cite the paper.

bibtex

@inproceedings{multinex2026,
  title     = {Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex},
  author    = {Alexandru Brateanu and Tingting Mu and Codruta O. Ancuti and Cosmin Ancuti},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}

albrateanu/multinex

Multinex: Lightweight Low-Light Image Enhancement via Multi-prior Retinex