A

Coupling Frequency-domain Stepwise Enhancement and Statistical Moment Adjustment for Single Image Dehazing

International Conference on Multimedia Retrieval, pp. 2295–2304

Abstract

Single Image Dehazing is a challenging ill-posed problem. While Transformers excel in long-range modeling, the low-pass characteristic of their self-attention further obscures high-frequency scene details attenuated by scattering. Furthermore, haze-degraded encoder features exhibit Mean-Elevation and Variance-Compression, leading to a significant statistical discrepancy with clear decoder features that hinders effective feature fusion. To address these limitations, this paper proposes the Frequency-Domain Stepwise and Moment Adjustment Network (FDSMA-Net). First, we design the High-Frequency Disentangled Attention (HFDA) mechanism, which explicitly decouples high-frequency components via attention inversion and utilizes a Dual Dynamic Frequency Modulation mechanism to calibrate and enhance details, effectively overcoming the spectral bias of standard attention. Second, the Moment-Driven Channel Fusion Attention (MDCFA) mechanism is introduced in the skip connections of the U-Net architecture. By explicitly aligning the first-order moment and second-order moment, MDCFA dynamically corrects the distribution shift caused by haze, ensuring statistically aligned feature fusion. Enabled by frequency disentanglement and moment alignment, FDSMA-Net excels on synthetic and real benchmarks, effectively recovering scene details and enhancing visual clarity. The source code of our proposed FDSMA-Net will be released at https://github.com/SWU-CS-MediaLab/FDSMA-Net.

Authors 4

  1. Southwest University

    Affiliation as printed

    Southwest University, Chongqing, Chongqing, China

  2. Southwest University

    Affiliation as printed

    Southwest University, Chongqing, Chongqing, China

  3. Leiden University

    Affiliation as printed

    Leiden University, Leiden, Netherlands

  4. Southwest University

    Affiliation as printed

    Southwest University, Chongqing, Chongqing, China

Cited by 1 stored of 1

1 result

No patents citing this paper on Lens.org (checked 2026-10-11).

References 56