The first mobile camera phone was sold only 20 years ago, when taking
pictures with one's phone was an oddity, and sharing pictures online was
unheard of. Today, the smartphone is more camera than phone. How did this
happen? This transformation was enabled by advances in computational
photography -the science and engineering of making great images from small form
factor, mobile cameras. Modern algorithmic and computing advances, including
machine learning, have changed the rules of photography, bringing to it new
modes of capture, post-processing, storage, and sharing. In this paper, we give
a brief history of mobile computational photography and describe some of the
key technological components, including burst photography, noise reduction, and
super-resolution. At each step, we may draw naive parallels to the human visual
system.

20 年前出售的第一款手机相机仅用来拍照，如今智能手机更多被用作相机；本文简要介绍了移动计算摄影的历史，以及其中包含的关键技术组件：爆发式拍摄、降噪和超分辨率等，并在每个步骤中与人类视觉系统进行类比。

移动计算摄影：一次之旅

Mobile Computational Photography: A Tour

While single-image super-resolution (SISR) has attracted substantial interest
in recent years, the proposed approaches are limited to learning image priors
in order to add high frequency details. In contrast, multi-frame
super-resolution (MFSR) offers the possibility of reconstructing rich details
by combining signal information from multiple shifted images. This key
advantage, along with the increasing popularity of burst photography, have made
MFSR an important problem for real-world applications.
We propose a novel architecture for the burst super-resolution task. Our
network takes multiple noisy RAW images as input, and generates a denoised,
super-resolved RGB image as output. This is achieved by explicitly aligning
deep embeddings of the input frames using pixel-wise optical flow. The
information from all frames are then adaptively merged using an attention-based
fusion module. In order to enable training and evaluation on real-world data,
we additionally introduce the BurstSR dataset, consisting of smartphone bursts
and high-resolution DSLR ground-truth. We perform comprehensive experimental
analysis, demonstrating the effectiveness of the proposed architecture.

本文提出了一种基于多帧图像超分辨率技术的新型神经网络结构，以像素级光流明确对齐输入帧的深嵌入，使用基于注意力的融合模块自适应地合并来自所有帧的信息，以产生高质量重建的 RGB 图像；同时介绍了 BurstSR 数据集，在真实世界数据上进行了全面实验分析，证明了该模型的有效性。

深度爆发超分辨率

Deep Burst Super-Resolution

Capturing images under extremely low-light conditions poses significant
challenges for the standard camera pipeline. Images become too dark and too
noisy, which makes traditional enhancement techniques almost impossible to
apply. Recently, learning-based approaches have shown very promising results
for this task since they have substantially more expressive capabilities to
allow for improved quality. Motivated by these studies, in this paper, we aim
to leverage burst photography to boost the performance and obtain much sharper
and more accurate RGB images from extremely dark raw images. The backbone of
our proposed framework is a novel coarse-to-fine network architecture that
generates high-quality outputs progressively. The coarse network predicts a
low-resolution, denoised raw image, which is then fed to the fine network to
recover fine-scale details and realistic textures. To further reduce the noise
level and improve the color accuracy, we extend this network to a permutation
invariant structure so that it takes a burst of low-light images as input and
merges information from multiple images at the feature-level. Our experiments
demonstrate that our approach leads to perceptually more pleasing results than
the state-of-the-art methods by producing more detailed and considerably higher
quality images.

本文提出了一种新的粗到细的神经网络架构，通过采用多张暗光照片进行融合并利用学习来处理图像，从而在极低光条件下提高图像质量

学习增强极暗图像的暴发摄影

Burst Photography for Learning to Enhance Extremely Dark Images

Modern inexpensive imaging sensors suffer from inherent hardware constraints
which often result in captured images of poor quality. Among the most common
ways to deal with such limitations is to rely on burst photography, which
nowadays acts as the backbone of all modern smartphone imaging applications. In
this work, we focus on the fact that every frame of a burst sequence can be
accurately described by a forward (physical) model. This in turn allows us to
restore a single image of higher quality from a sequence of low quality images
as the solution of an optimization problem. Inspired by an extension of the
gradient descent method that can handle non-smooth functions, namely the
proximal gradient descent, and modern deep learning techniques, we propose a
convolutional iterative network with a transparent architecture. Our network,
uses a burst of low quality image frames and is able to produce an output of
higher image quality recovering fine details which are not distinguishable in
any of the original burst frames. We focus both on the burst photography
pipeline as a whole, i.e. burst demosaicking and denoising, as well as on the
traditional Gaussian denoising task. The developed method demonstrates
consistent state-of-the art performance across the two tasks and as opposed to
other recent deep learning approaches does not have any inherent restrictions
either to the number of frames or their ordering. Code can be found at
this https URL

本文介绍了一种基于迭代卷积网络的低质量图像拍摄技术，该技术利用爆发式拍摄的图像帧进行高质量图像的恢复和去噪，并在爆发式拍摄流程中进行了多项改进，方法展现出稳定的性能，不受帧数、顺序等限制。