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 this https URL

WaveDH 是一个新颖而紧凑的 ConvNet，利用小波分解从特征层中提取低频和高频组件，以提高处理速度并实现高质量重建，在频率感知的基础上对特征进行了优化，以在计算成本和准确性之间取得有利的权衡，通过大量实验证明，WaveDH 方法在多个图像去雾基准上优于许多最先进的方法，并大幅降低了计算成本。

WaveDH: 基于小波子带引导的卷积神经网络在高效图像去雾中的应用

WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing

Efficient prediction of internet traffic is essential for ensuring proactive
management of computer networks. Nowadays, machine learning approaches show
promising performance in modeling real-world complex traffic. However, most
existing works assumed that model training and evaluation data came from
identical distribution. But in practice, there is a high probability that the
model will deal with data from a slightly or entirely unknown distribution in
the deployment phase. This paper investigated and evaluated machine learning
performances using eXtreme Gradient Boosting, Light Gradient Boosting Machine,
Stochastic Gradient Descent, Gradient Boosting Regressor, CatBoost Regressor,
and their stacked ensemble model using data from both identical and out-of
distribution. Also, we proposed a hybrid machine learning model integrating
wavelet decomposition for improving out-of-distribution prediction as
standalone models were unable to generalize very well. Our experimental results
show the best performance of the standalone ensemble model with an accuracy of
96.4%, while the hybrid ensemble model improved it by 1% for in-distribution
data. But its performance dropped significantly when tested with three
different datasets having a distribution shift than the training set. However,
our proposed hybrid model considerably reduces the performance gap between
identical and out-of-distribution evaluation compared with the standalone
model, indicating the decomposition technique's effectiveness in the case of
out-of-distribution generalization.

本文探讨和评估了使用多种机器学习算法对于相同分布和非同分布数据的表现，同时提出了一种混合机器学习模型，结合小波分解以提高泛化性能，实验结果显示，相比于独立模型，我们的混合模型在处理非同分布数据时表现更优。

基于小波的混合机器学习模型用于未知来源的互联网流量预测

Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution  Internet Traffic Prediction

We present a novel method for predicting accurate depths from monocular
images with high efficiency. This optimal efficiency is achieved by exploiting
wavelet decomposition, which is integrated in a fully differentiable
encoder-decoder architecture. We demonstrate that we can reconstruct
high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast
with previous works, we show that wavelet coefficients can be learned without
direct supervision on coefficients. Instead we supervise only the final depth
image that is reconstructed through the inverse wavelet transform. We
additionally show that wavelet coefficients can be learned in fully
self-supervised scenarios, without access to ground-truth depth. Finally, we
apply our method to different state-of-the-art monocular depth estimation
models, in each case giving similar or better results compared to the original
model, while requiring less than half the multiply-adds in the decoder network.
Code at this https URL

本文提出了一种基于小波分解的、高效预测单眼图像精确深度的新方法，并通过自监督学习和优化创新地实现了高保真深度图像重建和计算量优化。