What question did this study set out to answer?

The research aims to enhance image dehazing performance by developing a synergic expert modulation mechanism that effectively leverages context information.

February 12, 2026Visual Intelligence0 citationsOpen Access

Efficient image dehazing with synergic expert modulation

HSHao ShenAnhui University of Science and Technology XCXiaofeng CongSoutheast University HDHenghui DingNanyang Technological University

Key Points

The research aims to enhance image dehazing performance by developing a synergic expert modulation mechanism that effectively leverages context information.
Introduced a synergic expert modulation (SEM) mechanism with two types of expert modules: mixture of spatial experts (MSE) and mixture of frequency experts (MFE).
Implemented an adaptive learning process for hierarchical features through MSE modules.
Utilized frequency experts to perform spectral filtering and spectral learning on hazy images.
Integrated SEM blocks into the U-Net architecture to enhance efficiency.
Achieved state-of-the-art performance on multiple datasets for image dehazing tasks.
Demonstrated lower computational costs compared to existing methods.

Abstract

Abstract Recent developments in context modulation mechanisms have achieved significant improvements in performance, as well as better trade-offs between model accuracy and efficiency. These mechanisms operate on input through context modeling and then leverage these contexts to modulate projected input features. However, existing methods have limitations. First, they cannot adaptively learn the extracted hierarchical context or ignore the complementarity of the cross-scale context. Second, these methods do not adequately address the unique frequency characteristics of hazy images. In response, we propose a synergic expert modulation (SEM) mechanism to explicitly model context information. Specifically, the SEM consists primarily of two mixture of spatial experts (MSE) modules that handle features of different scales and one mixture of frequency experts (MFE) module that operates within the frequency domain. The MSE learns hierarchical features of various granularities in an adaptive manner, guided by multiple gating experts and a routing network. The MFE specializes in mining frequency contexts guided by multiple frequency experts and a routing network. At the micro level, each frequency expert operates in two stages: spectral filtering and spectral learning. The former performs mask filtering to enhance the weights of low-frequency components, and the latter performs Fourier amplitude and phase decoupled learning, thus promoting the removal of haze information and global context learning. Finally, the obtained contexts are integrated to modulate the projected feature, thereby significantly enhancing cross-domain feature synergies. The proposed network, referred to as the synergic expert modulation network, is constructed by inserting SEM-based building blocks into the U-Net architecture to increase efficiency. Extensive experiments demonstrate that our network achieves state-of-the-art performance on multiple datasets for the image dehazing task while incurring lower computational costs.

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