ADPCNet: Adaptive Deformable Peripheral Convolution for Efficient Image Dehazing

Single-image dehazing requires wide-range visibility estimation and local structure recovery under spatially varying degradation. Existing large-context models improve global reasoning, but they often incur heavy computation or lose sensitivity to irregular haze boundaries and attenuated details. To address these issues, we propose the Adaptive Deformable Peripheral Convolution Network (ADPCNet), a compact encoder-decoder that organizes dehazing into four coupled operations: conditional adaptive sharing for peripheral large-kernel context modeling, deformable sampling for geometry-aware aggregation, frequency-guided modulation for detail compensation, and dynamic multi-branch fusion for content-adaptive integration. The key idea is to separate broad haze estimation, structure alignment, and detail recovery within an efficient operator stack. Experiments on RESIDE, Dense-Haze, and NH-Haze show that ADPCNet achieves competitive paired-benchmark performance with 7.25 M parameters and 33.62 G FLOPs, reaching 40.89 dB/0.997 on SOTS-Indoor, 37.80 dB/0.996 on SOTS-Outdoor, 18.05 dB/0.679 on Dense-Haze, and 21.66 dB/0.815 on NH-Haze. The ablation and sensitivity results further support the contributions of the proposed modules and the selected kernel configuration. Overall, these results indicate that ADPCNet maintains a favorable quality-efficiency trade-off under the matched paired evaluation protocol.