Bursts of images exhibit significant self-similarity across both time and
space. This motivates a representation of the kernels as linear combinations of
a small set of basis elements. To this end, we introduce a novel basis
prediction network that, given an input burst, predicts a set of global basis
kernels -- shared within the image -- and the corresponding mixing coefficients
-- which are specific to individual pixels. Compared to state-of-the-art
techniques that output a large tensor of per-pixel spatiotemporal kernels, our
formulation substantially reduces the dimensionality of the network output.
This allows us to effectively exploit comparatively larger denoising kernels,
achieving both significant quality improvements (over 1dB PSNR) and faster
run-times over state-of-the-art methods.

通过预测一组全局基础核和相应的混合系数 -- 它们特定于个别像素，我们引入了一种新的基础预测网络来表示核，这些图像在时间和空间上显着自相似。相较于输出大量的每像素时空核的现有技术，我们的方法显著降低了网络输出的维度。这使得我们能够有效地利用较大的去噪核，从而获得比现有技术更快的运行时间和显著的质量改进（超过 1dB PSNR）。

基于预测网络的大核有效爆炸去噪

Basis Prediction Networks for Effective Burst Denoising with Large  Kernels

As demand for advanced photographic applications on hand-held devices grows,
these electronics require the capture of high quality depth. However, under
low-light conditions, most devices still suffer from low imaging quality and
inaccurate depth acquisition. To address the problem, we present a robust depth
estimation method from a short burst shot with varied intensity (i.e., Auto
Bracketing) or strong noise (i.e., High ISO). We introduce a geometric
transformation between flow and depth tailored for burst images, enabling our
learning-based multi-view stereo matching to be performed effectively. We then
describe our depth estimation pipeline that incorporates the geometric
transformation into our residual-flow network. It allows our framework to
produce an accurate depth map even with a bracketed image sequence. We
demonstrate that our method outperforms state-of-the-art methods for various
datasets captured by a smartphone and a DSLR camera. Moreover, we show that the
estimated depth is applicable for image quality enhancement and photographic
editing.

通过改良后的几何流深度估计技术和多视图立体匹配算法，我们提出了一种从短暴照片中进行深度估计的高精度方法，该方法适用于智能手机以及单反相机拍摄的不同数据集，估计的深度图可应用于图像质量提升和摄影后期处理。