Research on image feature matching under low-light environment based on IEA-DWT preprocessing

Zheng Dong; Feng Peng; Tan Zhou-nan; Tang Zeng; Wu Cheng-jin; Du Wei

doi:10.11884/HPLPB202638.250349

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Article Navigation > High Power Laser and Particle Beams > 2026 > Accepted Manuscript

Zheng Dong, Feng Peng, Tan Zhou-nan, et al. Research on image feature matching under low-light environment based on IEA-DWT preprocessing[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250349

Citation:

Zheng Dong, Feng Peng, Tan Zhou-nan, et al. Research on image feature matching under low-light environment based on IEA-DWT preprocessing[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250349

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Research on image feature matching under low-light environment based on IEA-DWT preprocessing

doi: 10.11884/HPLPB202638.250349

1.
College Information Engineering , Chongqing Electric Power College, Chongqing 400053, China
2.
College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China

Received Date: 2025-10-15
Accepted Date: 2026-01-22
Rev Recd Date: 2025-02-13

Available Online: 2026-03-28

Abstract

Abstract

Background
Low-light environments directly result in insufficient image brightness, obscured details, and significant noise, which lead to a sharp decline in the performance of traditional feature matching algorithms and a surge in the false matching rate.
Purpose
A novel preprocessing algorithm integrating the Illumination Enhancement Algorithm (IEA) and Discrete Wavelet Transform (DWT) is proposed, aiming to provide high-quality input for subsequent feature matching through multi-stage image optimization.
Methods
The algorithm utilizes IEA to adaptively enhance the overall brightness and local contrast of the image. Through DWT multi-scale decomposition, it preserves the main structure of the separated low-frequency components and performs threshold processing on high-frequency components in different directions, thereby achieving edge enhancement and noise reduction. Finally, the optimized image is input into the SURF algorithm to complete feature extraction and matching. To verify the effectiveness of the proposed algorithm, image feature matching experiments were conducted under extremely low-light, faintly low-light, and low-light environments.
Results
The experimental results show that compared with the single IEA or DWT preprocessing methods, the proposed algorithm achieves the most significant effect, with the average number of correct feature matching pairs reaching 261, which is a 6.53-fold increase.
Conclusions
The new preprocessing algorithm IEA-DWT can effectively solve the core problems of blurred details and noise interference in image feature matching in low-light environments, and provides reliable technical support for feature matching in low-light scenes.
- low-light environment,
- illumination enhancement algorithm,
- discrete wavelet transform,
- preprocessing

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References(22)

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