Neural network generalizability is becoming a broad research field due to the
increasing availability of datasets from different sources and for various
tasks. This issue is even wider when processing medical data, where a lack of
methodological standards causes large variations being provided by different
imaging centers or acquired with various devices and cofactors. To overcome
these limitations, we introduce a novel, generalizable, data- and task-agnostic
framework able to extract salient features from medical images. The proposed
quaternion wavelet network (QUAVE) can be easily integrated with any
pre-existing medical image analysis or synthesis task, and it can be involved
with real, quaternion, or hypercomplex-valued models, generalizing their
adoption to single-channel data. QUAVE first extracts different sub-bands
through the quaternion wavelet transform, resulting in both
low-frequency/approximation bands and high-frequency/fine-grained features.
Then, it weighs the most representative set of sub-bands to be involved as
input to any other neural model for image processing, replacing standard data
samples. We conduct an extensive experimental evaluation comprising different
datasets, diverse image analysis, and synthesis tasks including reconstruction,
segmentation, and modality translation. We also evaluate QUAVE in combination
with both real and quaternion-valued models. Results demonstrate the
effectiveness and the generalizability of the proposed framework that improves
network performance while being flexible to be adopted in manifold scenarios.

神经网络普遍性研究领域愈发广泛，鉴于来自不同源和任务的数据集日益丰富。这个问题在处理医学数据时尤为重要，因为缺乏方法标准会导致不同成像中心或使用不同设备和辅助因素提供的数据变化很大。为了克服这些限制，我们引入了一种新的、泛化的、数据和任务无关的框架，能够从医学图像中提取显著特征。所提出的四元波小波网络（QUAVE）可以轻松集成到任何现有的医学图像分析或综合任务中，它可以与实值、四元值或超复值模型结合使用，将它们的采用推广到单通道数据。QUAVE 首先通过四元波小波变换提取不同的子频带，得到低频 / 近似频带和高频 / 精细特征。然后，它对最具代表性的一组子频带进行权重，并将其作为任何其他图像处理的神经模型的输入，代替标准数据样本。我们进行了广泛的实验评估，包括不同的数据集、不同的图像分析和综合任务，包括重建、分割和模态转换。我们还评估了 QUAVE 与实值和四元值模型的组合。结果证明了所提出的框架的有效性和通用性，它能提高网络性能，并在多种场景中灵活使用。

通过四元波尔兹曼网络推广医学图像表示

Generalizing Medical Image Representations via Quaternion Wavelet  Networks

Lacking rich and realistic data, learned single image denoising algorithms
generalize poorly to real raw images that do not resemble the data used for
training. Although the problem can be alleviated by the heteroscedastic
Gaussian model for noise synthesis, the noise sources caused by digital camera
electronics are still largely overlooked, despite their significant effect on
raw measurement, especially under extremely low-light condition. To address
this issue, we present a highly accurate noise formation model based on the
characteristics of CMOS photosensors, thereby enabling us to synthesize
realistic samples that better match the physics of image formation process.
Given the proposed noise model, we additionally propose a method to calibrate
the noise parameters for available modern digital cameras, which is simple and
reproducible for any new device. We systematically study the generalizability
of a neural network trained with existing schemes, by introducing a new
low-light denoising dataset that covers many modern digital cameras from
diverse brands. Extensive empirical results collectively show that by utilizing
our proposed noise formation model, a network can reach the capability as if it
had been trained with rich real data, which demonstrates the effectiveness of
our noise formation model.

提出一种基于 CMOS 光电传感器特性的高精度噪声形成模型，并基于此模型提出了一种用于校准现有数码相机噪声参数的简单复制方法。此模型使神经网络具备了使用丰富真实数据进行训练的能力，并应用于覆盖多个品牌数码相机的新低光噪声数据集中，获得了显著的降噪效果。