Recently, Rissanen et al., (2022) have presented a new type of diffusion
process for generative modeling based on heat dissipation, or blurring, as an
alternative to isotropic Gaussian diffusion. Here, we show that blurring can
equivalently be defined through a Gaussian diffusion process with non-isotropic
noise. In making this connection, we bridge the gap between inverse heat
dissipation and denoising diffusion, and we shed light on the inductive bias
that results from this modeling choice. Finally, we propose a generalized class
of diffusion models that offers the best of both standard Gaussian denoising
diffusion and inverse heat dissipation, which we call Blurring Diffusion
Models.

本文介绍了一种新型的基于热扩散（或模糊）的生成建模扩散过程，它可以等效地通过具有非等向噪声的高斯扩 散过程来定义模糊，这一过程构成了一个推断性热反馈与去噪扩散之间的桥梁。最后，我们提出了一个广义的扩散模型类，它结 合了标准的高斯去噪扩散和推断热反馈的优点，称之为模糊扩散模型。

模糊扩散模型

Blurring Diffusion Models

Night photography typically suffers from both low light and blurring issues
due to the dim environment and the common use of long exposure. While existing
light enhancement and deblurring methods could deal with each problem
individually, a cascade of such methods cannot work harmoniously to cope well
with joint degradation of visibility and textures. Training an end-to-end
network is also infeasible as no paired data is available to characterize the
coexistence of low light and blurs. We address the problem by introducing a
novel data synthesis pipeline that models realistic low-light blurring
degradations. With the pipeline, we present the first large-scale dataset for
joint low-light enhancement and deblurring. The dataset, LOL-Blur, contains
12,000 low-blur/normal-sharp pairs with diverse darkness and motion blurs in
different scenarios. We further present an effective network, named LEDNet, to
perform joint low-light enhancement and deblurring. Our network is unique as it
is specially designed to consider the synergy between the two inter-connected
tasks. Both the proposed dataset and network provide a foundation for this
challenging joint task. Extensive experiments demonstrate the effectiveness of
our method on both synthetic and real-world datasets.

该研究通过引入新的数据合成流程来模拟真实的低照度模糊退化，提出了联合低照度增强和去模糊的大规模数据集 LOL-Blur，同时提出了一种名为 LEDNet 的有效网络，解决了夜间摄影低照度和模糊问题。

LEDNet: 低光弱化与去模糊联合处理

LEDNet: Joint Low-light Enhancement and Deblurring in the Dark

The mean-shift algorithm is a popular algorithm in computer vision and image
processing. It can also be cast as a minimum gamma-divergence estimation. In
this paper we focus on the "blurring" mean shift algorithm, which is one
version of the mean-shift process that successively blurs the dataset. The
analysis of the blurring mean-shift is relatively more complicated compared to
the nonblurring version, yet the algorithm convergence and the estimation
consistency have not been well studied in the literature. In this paper we
prove both the convergence and the consistency of the blurring mean-shift. We
also perform simulation studies to compare the efficiency of the blurring and
the nonblurring versions of the mean-shift algorithms. Our results show that
the blurring mean-shift has more efficiency.

本文中，我们聚焦于模糊均值漂移算法，并证明了其收敛性和一致性，并进行模拟研究比较了模糊和非模糊版本的均值漂移算法效率高低，结果表明，模糊均值漂移算法更加高效。