FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
This work addresses the challenge of efficient and accurate image dehazing for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of single image dehazing by proposing FAMED-Net, a fast and accurate multi-scale end-to-end network that reduces model complexity and improves computational efficiency while achieving superior restoration accuracy and cross-set generalization compared to state-of-the-art models on synthetic and real-world datasets.
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks. However, it remains challenging due to its ill-posed nature. Existing dehazing models tend to suffer from model overcomplexity and computational inefficiency or have limited representation capacity. To tackle these challenges, here we propose a fast and accurate multi-scale end-to-end dehazing network called FAMED-Net, which comprises encoders at three scales and a fusion module to efficiently and directly learn the haze-free image. Each encoder consists of cascaded and densely connected point-wise convolutional layers and pooling layers. Since no larger convolutional kernels are used and features are reused layer-by-layer, FAMED-Net is lightweight and computationally efficient. Thorough empirical studies on public synthetic datasets (including RESIDE) and real-world hazy images demonstrate the superiority of FAMED-Net over other representative state-of-the-art models with respect to model complexity, computational efficiency, restoration accuracy, and cross-set generalization. The code will be made publicly available.