We extend the blindspot model for self-supervised denoising to handle
Poisson-Gaussian noise and introduce an improved training scheme that avoids
hyperparameters and adapts the denoiser to the test data. Self-supervised
models for denoising learn to denoise from only noisy data and do not require
corresponding clean images, which are difficult or impossible to acquire in
some application areas of interest such as low-light microscopy. We introduce a
new training strategy to handle Poisson-Gaussian noise which is the standard
noise model for microscope images. Our new strategy eliminates hyperparameters
from the loss function, which is important in a self-supervised regime where no
ground truth data is available to guide hyperparameter tuning. We show how our
denoiser can be adapted to the test data to improve performance. Our
evaluations on microscope image denoising benchmarks validate our approach.

本文提出了一种改进的自监督去噪模型，该模型可以处理 Poisson-Gaussian 噪声，并且避免使用超参数，在低光显微镜等某些应用领域中取得了较好的性能表现，评估结果表明该模型优于其他基准模型。

自监督泊松 - 高斯去噪

Self-Supervised Poisson-Gaussian Denoising

Super-resolution (SR) has been widely used to convert low-resolution legacy
videos to high-resolution (HR) ones, to suit the increasing resolution of
displays (e.g. UHD TVs). However, it becomes easier for humans to notice motion
artifacts (e.g. motion judder) in HR videos being rendered on larger-sized
display devices. Thus, broadcasting standards support higher frame rates for
UHD (Ultra High Definition) videos (4K@60 fps, 8K@120 fps), meaning that
applying SR only is insufficient to produce genuine high quality videos. Hence,
to up-convert legacy videos for realistic applications, not only SR but also
video frame interpolation (VFI) is necessitated. In this paper, we first
propose a joint VFI-SR framework for up-scaling the spatio-temporal resolution
of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training
scheme with a multi-scale temporal loss that imposes temporal regularization on
the input video sequence, which can be applied to any general video-related
task. The proposed structure is analyzed in depth with extensive experiments.

本文提出了一个联合 VFI 和 SR 的框架，用于将 2K 30fps 的视频升级到 4K 60fps。其中，采用了一种新颖的训练方案，该方案使用多尺度时间损失对输入视频序列进行时间规范化，可以应用于任何一般的视频相关任务。通过大量实验对所提出的结构进行了深入研究。

FISR：具有多尺度时间损失的深度联合帧插值和超分辨率

FISR: Deep Joint Frame Interpolation and Super-Resolution with a  Multi-scale Temporal Loss

Massive multiple-input multiple-output (MIMO) systems deploying a large
number of antennas at the base station considerably increase the spectrum
efficiency by serving multiple users simultaneously without causing severe
interference. However, the advantage relies on the availability of the downlink
channel state information (CSI) of multiple users, which is still a challenge
in frequency-division-duplex transmission systems. This paper aims to solve
this problem by developing a full transceiver framework that includes downlink
channel training (or estimation), CSI feedback, and channel reconstruction
schemes. Our framework provides accurate reconstruction results for multiple
users with small amounts of training and feedback overhead. Specifically, we
first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP)
algorithm to extract the frequency-independent parameters (i.e., downtilts,
azimuths, and delays) from the uplink. Then, by leveraging the information from
these frequency-independent parameters, we develop an efficient downlink
training scheme to estimate the downlink channel gains for multiple users. This
training scheme offers an acceptable estimation error rate of the gains with a
limited pilot amount. Numerical results verify the precision of the eNOMP
algorithm and demonstrate that the sum-rate performance of the system using the
reconstructed downlink channel can approach that of the system using perfect
CSI.

本研究旨在通过开发全收发机制，包括下行信道训练（或估计），CSI 反馈和信道重建方案，解决大规模 MIMO 系统中多用户 CSI 的问题。我们的框架利用增强的 Newtonized 正交匹配追踪算法提取频率独立参数，然后开发了有效的下行训练方案，以估计多个用户的下行信道增益。数值结果验证了 eNOMP 算法的精度，并证明了使用重构的下行信道的系统的求和速率性能可以接近使用完美 CSI 的系统。