Multi-exposure high dynamic range (HDR) imaging reconstructs scenes with large illumination variations by fusing multiple low dynamic range images, but large exposure gaps and scene motion often lead to ghosting artifacts, luminance inconsistency, detail degradation, and frequency imbalance. To address these challenges, we propose FD-HDRMamba, a frequency-decoupled HDR reconstruction framework that separately models global low-frequency structures and local high-frequency details. The proposed method first performs implicit feature-level alignment to reduce exposure and motion discrepancies, and then decomposes aligned features into frequency components. The low-frequency branch uses Mamba and a low-frequency-aware FFN to capture long-range dependencies and maintain global luminance consistency, while the high-frequency branch adopts residual feature distillation to enhance textures and structural details. Experiments on benchmark datasets show that FD-HDRMamba achieves competitive or superior performance in both quantitative metrics and visual quality, validating the effectiveness of frequency-decoupled HDR reconstruction.
Zhehan Gong, Wei Wang, Xiao Wang et al.· IEEE Signal Processing Lette...· 0 citations
Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bottlenecks: reliance on ambiguous global representations and unchecked propagation of pseudo-label noise in an open-loop manner. To address these issues, we propose Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system. Structurally, we introduce Fine-grained Structural Decoupling (FSD) to extract discriminative body-part primitives as reliable spatial anchors, complementing ambiguous holistic silhouettes with spatially consistent structural details. Semantically, we design a Closed-loop Semantic Calibration (CSC) mechanism that reconstructs shared semantic prototypes at each epoch and feeds them back into the training loop, effectively filtering pseudo-label noise before the next clustering cycle. Through the reciprocal interaction between structural and semantic learning, SSRL achieves robust cross-modal representation. Extensive experiments demonstrate the competitive performance of SSRL against state-of-the-art USVI-ReID methods on both SYSU-MM01 and RegDB, notably surpassing several supervised counterparts on RegDB.
Moyao Tian, Shijia Liu, Yan Yang et al.· 0 citations