Work

Dual-Task Learning for Long-Range Classification in Single-Pixel Imaging Under Atmospheric Turbulence

Year: 2025

Type: article

Abstract: Unlike traditional imaging, single-pixel imaging (SPI) exhibits greater resistance to atmospheric turbulence. Therefore, we use SPI for long-range classification, in which atmospheric turbulence often cause significant degradation in performance. We propose a dual-task learning method for SPI classification. Specifically, we design the Long-Range Dual-Task Single-Pixel Network (LR-DTSPNet) to perform object classification and image restoration simultaneously, enhancing the model’s generalization and robustness. Attention mechanisms and residual convolutions are used to strengthen feature modeling and improve classification performance on low-resolution images. To improve the efficiency of SPI, low-resolution objects are used in this work. Experimental results on the DOTA remote sensing dataset demonstrate that our method significantly outperforms conventional object classification approaches. Furthermore, our approach holds promise for delivering high-quality images that are applicable to other computer vision tasks.

Source: Electronics

DOI: 10.3390/electronics14071355

Authors: Yusen Liao, Yin Cheng, Jun Ke

Institutions: Beijing Institute of Technology, Ministry of Education of the People's Republic of China

Language: English

Topic: Random lasers and scattering media

Subfield: Acoustics and Ultrasonics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China