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, faint 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.