  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.
