Blind-spot networks (BSN) have been prevalent network architectures in
self-supervised image denoising (SSID). Existing BSNs are mostly conducted with
convolution layers. Although transformers offer potential solutions to the
limitations of convolutions and have demonstrated success in various image
restoration tasks, their attention mechanisms may violate the blind-spot
requirement, thus restricting their applicability in SSID. In this paper, we
present a transformer-based blind-spot network (TBSN) by analyzing and
redesigning the transformer operators that meet the blind-spot requirement.
Specifically, TBSN follows the architectural principles of dilated BSNs, and
incorporates spatial as well as channel self-attention layers to enhance the
network capability. For spatial self-attention, an elaborate mask is applied to
the attention matrix to restrict its receptive field, thus mimicking the
dilated convolution. For channel self-attention, we observe that it may leak
the blind-spot information when the channel number is greater than spatial size
in the deep layers of multi-scale architectures. To eliminate this effect, we
divide the channel into several groups and perform channel attention
separately. Furthermore, we introduce a knowledge distillation strategy that
distills TBSN into smaller denoisers to improve computational efficiency while
maintaining performance. Extensive experiments on real-world image denoising
datasets show that TBSN largely extends the receptive field and exhibits
favorable performance against state-of-the-art SSID methods. The code and
pre-trained models will be publicly available at
this https URL

本文提出了基于 transformer 的盲点网络（TBSN），通过分析和重新设计满足盲点要求的 transformer 操作符，扩展了其感受野，实现了有利于自监督图像去噪（SSID）任务处理的效果，并引入知识蒸馏策略提高计算效率。

TBSN: 基于 Transformer 的自监督图像降噪的盲点网络

TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image  Denoising

Hyperspectral anomaly detection (HAD) aims to localize pixel points whose
spectral features differ from the background. HAD is essential in scenarios of
unknown or camouflaged target features, such as water quality monitoring, crop
growth monitoring and camouflaged target detection, where prior information of
targets is difficult to obtain. Existing HAD methods aim to objectively detect
and distinguish background and anomalous spectra, which can be achieved almost
effortlessly by human perception. However, the underlying processes of human
visual perception are thought to be quite complex. In this paper, we analyze
hyperspectral image (HSI) features under human visual perception, and transfer
the solution process of HAD to the more robust feature space for the first
time. Specifically, we propose a small target aware detector (STAD), which
introduces saliency maps to capture HSI features closer to human visual
perception. STAD not only extracts more anomalous representations, but also
reduces the impact of low-confidence regions through a proposed small target
filter (STF). Furthermore, considering the possibility of HAD algorithms being
applied to edge devices, we propose a full connected network to convolutional
network knowledge distillation strategy. It can learn the spectral and spatial
features of the HSI while lightening the network. We train the network on the
HAD100 training set and validate the proposed method on the HAD100 test set.
Our method provides a new solution space for HAD that is closer to human visual
perception with high confidence. Sufficient experiments on real HSI with
multiple method comparisons demonstrate the excellent performance and unique
potential of the proposed method. The code is available at
this https URL

本文分析了人类视觉感知下的高光谱影像特征，并首次将异常检测 (HAD) 的解决过程转移到了更稳健的特征空间。我们提出了一种小目标感知检测器 (STAD)，它引入了显著性图来捕捉更接近人类视觉感知的高光谱影像特征。此外，我们还提出了一种全连接网络到卷积网络的知识蒸馏策略，以适应 HAD 算法应用于边缘设备的可能性。我们在 HAD100 训练集上训练了网络，并在 HAD100 测试集上验证了所提出方法。经过充分的实验证明，我们的方法在真实高光谱影像上表现出卓越的性能和独特的潜力。