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Updated: Sep 19, 2025

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Deep Learning for Low-Light Vision: A Comprehensive Survey.

Qian Zhao, Gang Li, Bin He

IEEE Transactions on Neural Networks and Learning Systems

|June 2, 2025

Summary

This summary is machine-generated.

This survey covers recent advancements in low-light vision, focusing on both image enhancement and object detection. It benchmarks methods and discusses future research directions for improved low-light visual recognition.

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Visual recognition in low-light conditions is difficult due to image degradations like noise and blur.
Deep learning has spurred significant interest in low-light vision tasks.
Existing surveys often address low-light image enhancement (LLIE) or normal-light recognition separately, leaving a gap in comprehensive low-light vision task reviews.

Purpose of the Study:

To provide a comprehensive survey of recent progress in low-light vision.
To cover methods, datasets, and evaluation metrics for low-light vision.
To analyze low-light vision from both visual quality and recognition quality perspectives.

Main Methods:

Surveying recent low-light image enhancement (LLIE) methods.
Reviewing low-light object detection techniques using a novel categorization.
Conducting quantitative benchmarking of various methods on standard low-light datasets.

Main Results:

A broad overview of state-of-the-art LLIE techniques.
An organized review of deep learning-based low-light object detection.
Empirical performance comparisons of different low-light vision approaches.

Conclusions:

Low-light vision is a rapidly evolving field with distinct visual quality and recognition challenges.
Benchmarking reveals performance variations across methods and datasets.
Further research is needed to address existing challenges and explore future directions in low-light vision.