DMSH-Net: Depth-aware multi-scale hybrid vision network for image dehazing

Chenping Zhao, Jun Li, Yingjun Wang, Zuhua Guo and Xiaoyue Li

PLOS ONE, 2026, vol. 21, issue 8, 1-17

Abstract: Single-image dehazing remains a challenging low-level vision task because haze degradation is inherently depth-dependent and spatially non-uniform. To address this problem, we propose DMSH-Net, a Depth-Aware Multi-Scale Hybrid Vision Network specifically designed for robust single-image dehazing. DMSH-Net is designed to implicitly capture haze variations through hierarchical feature recalibration, nonlinear residual refinement, and multi-scale contextual aggregation. Specifically, we introduce a redesigned convolutional squeeze-and-excitation attention (CSEA) module, which replaces fully connected transformations with convolutional operations and global average pooling to jointly model channel dependencies and spatial context. Building on CSEA, a nonlinear CSEA-coupled residual block (NCCRB) is developed to enhance local feature representation and improve adaptability to haze with varying densities. Furthermore, a multi-scale dilated convolution bottleneck is incorporated to enlarge the receptive field and aggregate haze-aware contextual information across multiple spatial scales, thereby improving the restoration of regions with varying scene depths. Extensive experiments on standard benchmarks demonstrate that DMSH-Net consistently achieves superior quantitative performance across full-reference and no-reference evaluations, thereby validating its robustness in complex real-world dehazing scenarios.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0352586

DOI: 10.1371/journal.pone.0352586

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