Image restoration networks are usually comprised of an encoder and a decoder,
responsible for aggregating image content from noisy, distorted data and to
restore clean, undistorted images, respectively. Data aggregation as well as
high-resolution image generation both usually come at the risk of involving
aliases, i.e.~standard architectures put their ability to reconstruct the model
input in jeopardy to reach high PSNR values on validation data. The price to be
paid is low model robustness. In this work, we show that simply providing
alias-free paths in state-of-the-art reconstruction transformers supports
improved model robustness at low costs on the restoration performance. We do so
by proposing BOA-Restormer, a transformer-based image restoration model that
executes downsampling and upsampling operations partly in the frequency domain
to ensure alias-free paths along the entire model while potentially preserving
all relevant high-frequency information.

通过在传统的重建变压器中提供无混叠路径，BOA-Restormer 是一种基于变压器的图像恢复模型，能够同时改善模型稳健性和恢复性能。

警惕别名 - 信号保留对于强健的图像恢复至关重要

Beware of Aliases -- Signal Preservation is Crucial for Robust Image  Restoration

In this paper, we explore the role of Instance Normalization in low-level
vision tasks. Specifically, we present a novel block: Half Instance
Normalization Block (HIN Block), to boost the performance of image restoration
networks. Based on HIN Block, we design a simple and powerful multi-stage
network named HINet, which consists of two subnetworks. With the help of HIN
Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration
tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD
dataset, with only 7.5% and 30% of its multiplier-accumulator operations
(MACs), 6.8 times and 2.9 times speedup respectively. For image deblurring, we
get comparable performance with 22.5% of its MACs and 3.3 times speedup on REDS
and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the
average result of multiple datasets with 1.4 times speedup. With HINet, we won
1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG
Artifacts, with a PSNR of 29.70. The code is available at
this https URL

本文探讨了 Instance Normalization 在低级别视觉任务中的作用。通过引入 HIN Block，我们设计了一个简单而强大的多级网络 HINet，该网络在各种图像恢复任务上超越了现有技术。

HINet：半实例归一化网络用于图像恢复

HINet: Half Instance Normalization Network for Image Restoration

Convolutional Neural Networks have achieved significant success across
multiple computer vision tasks. However, they are vulnerable to carefully
crafted, human-imperceptible adversarial noise patterns which constrain their
deployment in critical security-sensitive systems. This paper proposes a
computationally efficient image enhancement approach that provides a strong
defense mechanism to effectively mitigate the effect of such adversarial
perturbations. We show that deep image restoration networks learn mapping
functions that can bring off-the-manifold adversarial samples onto the natural
image manifold, thus restoring classification towards correct classes. A
distinguishing feature of our approach is that, in addition to providing
robustness against attacks, it simultaneously enhances image quality and
retains models performance on clean images. Furthermore, the proposed method
does not modify the classifier or requires a separate mechanism to detect
adversarial images. The effectiveness of the scheme has been demonstrated
through extensive experiments, where it has proven a strong defense in gray-box
settings. The proposed scheme is simple and has the following advantages: (1)
it does not require any model training or parameter optimization, (2) it
complements other existing defense mechanisms, (3) it is agnostic to the
attacked model and attack type and (4) it provides superior performance across
all popular attack algorithms. Our codes are publicly available at
this https URL

本文提出了一个有效的图像增强方法，通过深度图像恢复网络来将离散的对抗噪声样本转换回自然图像流形，从而提高对抗性的鲁棒性，同时提高图像质量和保持干净图像上的性能表现。