We consider using {\bf\em untrained neural networks} to solve the
reconstruction problem of snapshot compressive imaging (SCI), which uses a
two-dimensional (2D) detector to capture a high-dimensional (usually 3D)
data-cube in a compressed manner. Various SCI systems have been built in recent
years to capture data such as high-speed videos, hyperspectral images, and the
state-of-the-art reconstruction is obtained by the deep neural networks.
However, most of these networks are trained in an end-to-end manner by a large
amount of corpus with sometimes simulated ground truth, measurement pairs. In
this paper, inspired by the untrained neural networks such as deep image priors
(DIP) and deep decoders, we develop a framework by integrating DIP into the
plug-and-play regime, leading to a self-supervised network for spectral SCI
reconstruction. Extensive synthetic and real data results show that the
proposed algorithm without training is capable of achieving competitive results
to the training based networks. Furthermore, by integrating the proposed method
with a pre-trained deep denoising prior, we have achieved state-of-the-art
results. {Our code is available at
https://github.com/mengziyi64/CASSI-Self-Supervised.}

本文提出了一种结合 DIP 的插入式自监督网络框架，使用未经训练的神经网络解决了快照压缩成像的光谱重建问题，实验证明该算法可以在不使用大量模拟数据的情况下取得与基于训练的网络相媲美的性能，并可与预训练的深度去噪先验结合以获得最先进的结果。

自监督神经网络用于光谱瞬间压缩成像

Self-supervised Neural Networks for Spectral Snapshot Compressive  Imaging

An important scenario for image quality assessment (IQA) is to evaluate image
restoration (IR) algorithms. The state-of-the-art approaches adopt a
full-reference paradigm that compares restored images with their corresponding
pristine-quality images. However, pristine-quality images are usually
unavailable in blind image restoration tasks and real-world scenarios. In this
paper, we propose a practical solution named degraded-reference IQA (DR-IQA),
which exploits the inputs of IR models, degraded images, as references.
Specifically, we extract reference information from degraded images by
distilling knowledge from pristine-quality images. The distillation is achieved
through learning a reference space, where various degraded images are
encouraged to share the same feature statistics with pristine-quality images.
And the reference space is optimized to capture deep image priors that are
useful for quality assessment. Note that pristine-quality images are only used
during training. Our work provides a powerful and differentiable metric for
blind IRs, especially for GAN-based methods. Extensive experiments show that
our results can even be close to the performance of full-reference settings.

本文介绍了一种命名为 DR-IQA 的实用解决方案，利用失真图像作为参考，通过学习参考空间来提取参考信息并捕获有用于质量评估的深度图像先验，从而为盲目的图像恢复任务提供强大的可微指标，同时在 GAN-based 方法中表现良好。