Related Experiment Video
Updated: Aug 5, 2026
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
PSRNet: a phase-guided frequency-domain structure reconstruction network for RGB-T salient object detection
Feng Xie1, Junhong Zhou1, Feng Gao1
1School of Mathematics and Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, China.
Introduction:
RGB-T salient object detection remains challenging because visible and thermal features often show structural shifts, weak thermal boundaries, and background interference.
Methods:
This study proposes PSRNet, a phase-guided frequency-domain structure reconstruction network. RGB and thermal features are decomposed into amplitude and latent-phase components in the Fourier domain. Reliable cross-modal structural cues are aligned through amplitude-weighted phase consistency, and boundary-related high-frequency responses are reconstructed with a bounded Gaussian high-pass gate before adaptive phase-modulated fusion and multi-scale supervision.
Results:
On VT1000, PSRNet achieved an S-measure of 0.903, an MAE of 0.028, and an F-measure of 0.794 at threshold 0.80, with a boundary IoU of 0.862.
Discussion:
The results indicate improved structural preservation and boundary recovery under challenging RGB-T conditions.
