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HPCDNet: Hybrid position coding and dual-frquency domain transform network for low-light image enhancement.

Mingju Chen^1,2, Hongyang Li¹, Hongming Peng¹

¹School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China.

Mathematical Biosciences and Engineering : MBE

|March 8, 2024

Summary

This study introduces HPCDNet, a novel deep learning network for low-light image enhancement. It effectively utilizes positional and frequency domain information to improve image visibility, contrast, and detail preservation.

Keywords:

cosine transform image enhancement position coding self-attention wavelet transform

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Low-light image enhancement (LLIE) aims to restore visibility in images captured under poor illumination.
Existing LLIE methods often fail to fully leverage spatial positional and frequency domain information.
This limits their ability to accurately reconstruct details and improve visual quality.

Purpose of the Study:

To propose an end-to-end network, HPCDNet, for effective low-light image enhancement.
To integrate hybrid positional coding and frequency domain recovery into a unified framework.
To improve the visual quality, detail preservation, and texture fidelity of enhanced low-light images.

Main Methods:

HPCDNet employs a hybrid positional coding technique within its self-attention mechanism to retain spatial information.
Discrete wavelet and cosine transforms are used to recover lost frequency domain information.
A dual-attention module adaptively weights and merges the recovered frequency domain features.

Main Results:

The proposed HPCDNet significantly enhances visibility, contrast, and color properties of low-light images.
The method demonstrates superior preservation of image details and textures compared to existing techniques.
Experimental results validate the effectiveness of integrating positional and frequency domain information.

Conclusions:

HPCDNet offers an advanced solution for low-light image enhancement by effectively utilizing both spatial and frequency domain features.
The network's hybrid positional coding and dual-attention module contribute to improved image reconstruction.
This approach advances the state-of-the-art in low-light image enhancement, yielding more natural and visually appealing results.