SLRNet: Super Lightweight Residual Network for Real-Time Image Dehazing
TL;DR
This work proposes SLRNet, Super Lightweight Residual Network, a high efficient-yet-effective end-to-end dehazing architecture that integrates a novel Adaptive Feature Unit that automatically adjusts channel-wise features through a lightweight gating mechanism, coupled with compact residual blocks to preserve critical structural information.
Abstract
Image dehazing aims to generate the haze-free images from the hazy observation images. While recent deep learning approaches achieve impressive restoration quality, they suffer from excessive computational complexity and model size, hindering practical applications for real-world deployment on resource-constrained edge devices. To address the limitation, lightweight models are proposed to this end but compromise on dehazing performance. To bridge this gap, we propose SLRNet, Super Lightweight Residual Network, a high efficient-yet-effective end-to-end dehazing architecture. SLRNet integrates a novel Adaptive Feature Unit that automatically adjusts channel-wise features through a lightweight gating mechanism, coupled with compact residual blocks to preserve critical structural information. Unlike standard channel attention mechanisms that discard spatial information, our AFU employs an asymmetric split strategy to simultaneously preserve local texture details and capture global haze density. Our design emphasizes minimal parameter count and low latency without sacrificing perceptual quality. Experiments are carried out across standard benchmarks, showing that our proposed SLRNet demonstrates remarkable performance by achieving state-of-the-art efficiency-accuracy trade-offs compared to existing works, while maintaining robust generalization to real-world haze despite the synthetic-to-real domain gap. The codes are released in https://anonymous.4open.science/r/SLRNet.