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LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement

Introduction

This is a Pytorch implement of “LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement” (ECCV 2024)

we propose a semi-supervised method based on latent mean-teacher and Gaussian process, named LMT-GP. We first design a latent mean-teacher framework that integrates both labeled and unlabeled data, as well as their latent vectors, into model training. Meanwhile, we use a mean-teacher-assisted Gaussian process learning strategy to establish a connection between the latent and pseudo-latent vectors obtained from the labeled and unlabeled data. To guide the learning process, we utilize an assisted Gaussian process regression (GPR) loss function. Furthermore, we design a pseudo-label adaptation module (PAM) to ensure the reliability of the network learning. <img src="mainView.png" alt="示例图片" />

Installation

We are preparing an extended version. The core code "trainer.py" and "./loss/GP.py" is coming soon.

The checkpoint can be used for inference (BaiduYun).

Dataset Structure

    ├── data 
    |   ├── Labeled data # Training  
    |   |   ├── our485        
    |   |   |   ├── low   
    |   |   |   └── high
    |   ├── Unlabeled data # Training  
    |   |   ├── low       
    |   └── val  # Testing
    |   |   ├── low   
    |   |   └── high

Quick Start

Train

python train.py

Test

python test.py

Acknowledgements