While recent years have witnessed a dramatic upsurge of exploiting deep
neural networks toward solving image denoising, existing methods mostly rely on
simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG
compression noise and camera sensor noise, and a general-purpose blind
denoising method for real images remains unsolved. In this paper, we attempt to
solve this problem from the perspective of network architecture design and
training data synthesis. Specifically, for the network architecture design, we
propose a swin-conv block to incorporate the local modeling ability of residual
convolutional layer and non-local modeling ability of swin transformer block,
and then plug it as the main building block into the widely-used image-to-image
translation UNet architecture. For the training data synthesis, we design a
practical noise degradation model which takes into consideration different
kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and
processed camera sensor noises) and resizing, and also involves a random
shuffle strategy and a double degradation strategy. Extensive experiments on
AGWN removal and real image denoising demonstrate that the new network
architecture design achieves state-of-the-art performance and the new
degradation model can help to significantly improve the practicability. We
believe our work can provide useful insights into current denoising research.

本文旨在从网络架构设计和训练数据合成的角度解决当前真实图像去噪的问题，提出了一种新的网络架构设计和噪声退化模型，分别插入到 UNet 框架中从而达到当前最先进的性能水平。

基于 Swin-Conv-UNet 和数据合成实现的实用式盲去噪

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Recently, deep learning-based single image reflection separation methods have
been exploited widely. To benefit the learning approach, a large number of
training image pairs (i.e., with and without reflections) were synthesized in
various ways, yet they are away from a physically-based direction. In this
paper, physically based rendering is used for faithfully synthesizing the
required training images, and a corresponding network structure and loss term
are proposed. We utilize existing RGBD/RGB images to estimate meshes, then
physically simulate the light transportation between meshes, glass, and lens
with path tracing to synthesize training data, which successfully reproduce the
spatially variant anisotropic visual effect of glass reflection. For guiding
the separation better, we additionally consider a module, backtrack network
(BT-net) for backtracking the reflections, which removes complicated ghosting,
attenuation, blurred and defocused effect of glass/lens. This enables obtaining
a priori information before having the distortion. The proposed method
considering additional a priori information with physically simulated training
data is validated with various real reflection images and shows visually
pleasant and numerical advantages compared with state-of-the-art techniques.

本文提出了基于物理渲染的方法，通过 RGBD/RGB 图像确定网格，进行光传输仿真，合成训练数据并在此基础上进行基于深度学习的单张图像反射分离。同时，引入回溯网络模块（BT-net）来去除图像噪点、模糊和偏焦等问题。实验结果表明，相比于现有技术，本文方法在视觉效果和数值优化上都具有明显优势。