As the popularity of mobile photography continues to grow, considerable
effort is being invested in the reconstruction of degraded images. Due to the
spatial variation in optical aberrations, which cannot be avoided during the
lens design process, recent commercial cameras have shifted some of these
correction tasks from optical design to postprocessing systems. However,
without engaging with the optical parameters, these systems only achieve
limited correction for aberrations.In this work, we propose a practical method
for recovering the degradation caused by optical aberrations. Specifically, we
establish an imaging simulation system based on our proposed optical point
spread function model. Given the optical parameters of the camera, it generates
the imaging results of these specific devices. To perform the restoration, we
design a spatial-adaptive network model on synthetic data pairs generated by
the imaging simulation system, eliminating the overhead of capturing training
data by a large amount of shooting and registration. Moreover, we
comprehensively evaluate the proposed method in simulations and experimentally
with a customized digital-single-lens-reflex (DSLR) camera lens and HUAWEI
HONOR 20, respectively. The experiments demonstrate that our solution
successfully removes spatially variant blur and color dispersion. When compared
with the state-of-the-art deblur methods, the proposed approach achieves better
results with a lower computational overhead. Moreover, the reconstruction
technique does not introduce artificial texture and is convenient to transfer
to current commercial cameras. Project Page:
https://github.com/TanGeeGo/ImagingSimulation.

本研究提出了一种用于恢复因光学像差导致的图像退化的实用方法，通过基于我们提出的光学点扩散函数模型的成像模拟系统，生成特定设备的成像结果，并在合成数据对上设计空间自适应网络模型，消除了大量拍摄和注册训练数据的开销。实验结果表明，该方法成功地消除了空间变异模糊和色散，并具有更低的计算开销和便于转移到当前商业相机。

使用成像模拟进行后处理的光学像差校正

Optical Aberration Correction in Postprocessing using Imaging Simulation

As the revolutionary improvement being made on the performance of smartphones
over the last decade, mobile photography becomes one of the most common
practices among the majority of smartphone users. However, due to the limited
size of camera sensors on phone, the photographed image is still visually
distinct to the one taken by the digital single-lens reflex (DSLR) camera. To
narrow this performance gap, one is to redesign the camera image signal
processor (ISP) to improve the image quality. Owing to the rapid rise of deep
learning, recent works resort to the deep convolutional neural network (CNN) to
develop a sophisticated data-driven ISP that directly maps the phone-captured
image to the DSLR-captured one. In this paper, we introduce a novel network
that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to
tackle this learnable image ISP problem. By adding the wavelet transform, our
proposed method enables us to restore favorable image details from RAW
information and achieve a larger receptive field while remaining high
efficiency in terms of computational cost. The global context block is adopted
in our method to learn the non-local color mapping for the generation of
appealing RGB images. More importantly, this block alleviates the influence of
image misalignment occurred on the provided dataset. Experimental results
indicate the advances of our design in both qualitative and quantitative
measurements. The code is available publically.

本论文介绍了一种利用注意机制和小波变换，名为 AWNet 的新型网络，用于解决可学习的图像 ISP 问题。实验结果表明，该方法在 qualitatif 和 quantitatif measurements 方面都取得了进展。

AWNet：基于注意力小波网络的图像 ISP

AWNet: Attentive Wavelet Network for Image ISP

As the popularity of mobile photography is growing constantly, lots of
efforts are being invested now into building complex hand-crafted camera ISP
solutions. In this work, we demonstrate that even the most sophisticated ISP
pipelines can be replaced with a single end-to-end deep learning model trained
without any prior knowledge about the sensor and optics used in a particular
device. For this, we present PyNET, a novel pyramidal CNN architecture designed
for fine-grained image restoration that implicitly learns to perform all ISP
steps such as image demosaicing, denoising, white balancing, color and contrast
correction, demoireing, etc. The model is trained to convert RAW Bayer data
obtained directly from mobile camera sensor into photos captured with a
professional high-end DSLR camera, making the solution independent of any
particular mobile ISP implementation. To validate the proposed approach on the
real data, we collected a large-scale dataset consisting of 10 thousand
full-resolution RAW-RGB image pairs captured in the wild with the Huawei P20
cameraphone (12.3 MP Sony Exmor IMX380 sensor) and Canon 5D Mark IV DSLR. The
experiments demonstrate that the proposed solution can easily get to the level
of the embedded P20's ISP pipeline that, unlike our approach, is combining the
data from two (RGB + B/W) camera sensors. The dataset, pre-trained models and
codes used in this paper are available on the project website.

研究表明，使用 PyNET 可以使用单个端到端深度学习模型取代复杂的手工设计相机 ISP 解决方案，实现精细图像恢复，并将移动相机传感器直接获得的 RAW Bayer 数据转换为使用专业高端 DSLR 相机采集的相片，无需了解特定的移动 ISP 实现。