The surge in interest regarding image dehazing has led to notable advancements in deep learning-based single image dehazing approaches, exhibiting impressive performance in recent studies. Despite these strides, many existing methods fall short in meeting the efficiency demands of practical applications. In this paper, we introduce WaveDH, a novel and compact ConvNet designed to address this efficiency gap in image dehazing. Our WaveDH leverages wavelet sub-bands for guided up-and-downsampling and frequency-aware feature refinement. The key idea lies in utilizing wavelet decomposition to extract low-and-high frequency components from feature levels, allowing for faster processing while upholding high-quality reconstruction. The downsampling block employs a novel squeeze-and-attention scheme to optimize the feature downsampling process in a structurally compact manner through wavelet domain learning, preserving discriminative features while discarding noise components. In our upsampling block, we introduce a dual-upsample and fusion mechanism to enhance high-frequency component awareness, aiding in the reconstruction of high-frequency details. Departing from conventional dehazing methods that treat low-and-high frequency components equally, our feature refinement block strategically processes features with a frequency-aware approach. By employing a coarse-to-fine methodology, it not only refines the details at frequency levels but also significantly optimizes computational costs. The refinement is performed in a maximum 8x downsampled feature space, striking a favorable efficiency-vs-accuracy trade-off. Extensive experiments demonstrate that our method, WaveDH, outperforms many state-of-the-art methods on several image dehazing benchmarks with significantly reduced computational costs. Our code is available at https://github.com/AwesomeHwang/WaveDH.

翻译：近年来，图像去雾领域的研究兴趣激增，基于深度学习的单幅图像去雾方法取得了显著进展，并在近期研究中展现出令人印象深刻的性能。尽管取得了这些进步，但许多现有方法仍难以满足实际应用中的效率需求。本文提出WaveDH，一种新颖且紧凑的卷积网络，旨在解决图像去雾中的效率瓶颈。我们的WaveDH利用小波子带进行引导的上下采样和频率感知特征细化。其核心思想在于利用小波分解从特征层级提取低频与高频分量，从而在保持高质量重建的同时实现更快的处理速度。下采样模块采用新颖的压缩-注意力机制，通过小波域学习以结构紧凑的方式优化特征下采样过程，在保留判别性特征的同时剔除噪声成分。在上采样模块中，我们引入了双上采样与融合机制以增强高频分量感知能力，有助于重建高频细节。不同于传统去雾方法对低频与高频分量进行同等处理，我们的特征细化模块采用频率感知策略对特征进行针对性处理。通过采用由粗到细的方法，该模块不仅能在频率层级上细化细节，还能显著优化计算成本。细化过程在最大8倍下采样的特征空间中进行，实现了良好的效率与精度平衡。大量实验表明，我们的WaveDH方法在多个图像去雾基准测试中超越了众多先进方法，同时显著降低了计算成本。代码已开源：https://github.com/AwesomeHwang/WaveDH。