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Super-Pixel Guided Low-Light Images Enhancement with Features Restoration.

Xiaoming Liu¹, Yan Yang¹, Yuanhong Zhong¹

¹School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces a novel low-light image enhancement method using Convolutional Neural Networks (CNNs) and Attentive Neural Processes (ANPs). The technique effectively improves visual quality and aids subsequent tasks like target detection.

Keywords:

Image enhancement attentive neural processes low-light super-pixel segmentation

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Low-light image enhancement is crucial for visual perception and downstream tasks like object detection.
Existing methods struggle to balance visual quality with feature preservation for subsequent analysis.

Purpose of the Study:

To develop an effective low-light image enhancement technique that balances visual perception and utility for high-level tasks.
To introduce a novel approach combining Convolutional Neural Networks (CNNs), super-pixel segmentation, and Attentive Neural Processes (ANPs).

Main Methods:

Utilizing shallow CNNs for initial feature restoration in low-light images.
Applying super-pixel segmentation to group similar image regions.
Employing Attentive Neural Processes (ANPs) for localized enhancement within super-pixels.

Main Results:

Achieved high scores in Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE).
Demonstrated superior performance in Scale-Invariant Feature Transform (SIFT) feature detection and target detection tasks.
Validated effectiveness on both synthetic and real-world low-light images.

Conclusions:

The proposed method significantly enhances low-light images, improving both visual quality and feature information.
This approach provides a robust foundation for subsequent computer vision tasks, outperforming existing methods.