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ADPCNet: Adaptive Deformable Peripheral Convolution for Efficient Image Dehazing.

Zhihao Wang¹, Yunjie Zhu¹, Xiaolong Zheng¹

¹College of Information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.

Journal of Imaging

|June 25, 2026

Summary

This summary is machine-generated.

We developed the Adaptive Deformable Peripheral Convolution Network (ADPCNet) for efficient single-image dehazing. This novel network effectively estimates visibility and recovers local structures, achieving high performance with fewer parameters.

Keywords:

deformable sampling efficient neural networks frequency-guided modulation image dehazing image restoration large-kernel convolution peripheral convolution

Related Experiment Videos

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Single-image dehazing is challenging due to spatially varying degradation.
Existing large-context models often suffer from high computational costs or loss of fine details.

Purpose of the Study:

To propose an efficient and effective single-image dehazing method.
To address limitations of current models in handling complex haze patterns and preserving image details.

Main Methods:

Introduced the Adaptive Deformable Peripheral Convolution Network (ADPCNet), a compact encoder-decoder architecture.
Employed conditional adaptive sharing for context modeling, deformable sampling for aggregation, frequency-guided modulation for detail compensation, and dynamic multi-branch fusion.
Separated haze estimation, structure alignment, and detail recovery into an efficient operator stack.

Main Results:

ADPCNet achieved competitive performance on RESIDE, Dense-Haze, and NH-Haze datasets.
Demonstrated strong results on SOTS-Indoor (40.89 dB/0.997) and SOTS-Outdoor (37.80 dB/0.996).
Achieved a favorable quality-efficiency trade-off with 7.25 M parameters and 33.62 G FLOPs.

Conclusions:

ADPCNet offers an effective solution for single-image dehazing.
The proposed modules contribute significantly to improved performance and efficiency.
The network maintains a good balance between image quality and computational cost.