Popular methods usually use a degradation model in a supervised way to learn
a watermark removal model. However, it is true that reference images are
difficult to obtain in the real world, as well as collected images by cameras
suffer from noise. To overcome these drawbacks, we propose a perceptive
self-supervised learning network for noisy image watermark removal (PSLNet) in
this paper. PSLNet depends on a parallel network to remove noise and
watermarks. The upper network uses task decomposition ideas to remove noise and
watermarks in sequence. The lower network utilizes the degradation model idea
to simultaneously remove noise and watermarks. Specifically, mentioned paired
watermark images are obtained in a self supervised way, and paired noisy images
(i.e., noisy and reference images) are obtained in a supervised way. To enhance
the clarity of obtained images, interacting two sub-networks and fusing
obtained clean images are used to improve the effects of image watermark
removal in terms of structural information and pixel enhancement. Taking into
texture information account, a mixed loss uses obtained images and features to
achieve a robust model of noisy image watermark removal. Comprehensive
experiments show that our proposed method is very effective in comparison with
popular convolutional neural networks (CNNs) for noisy image watermark removal.
Codes can be obtained at this https URL

本文提出了一种用于嘈杂图像水印移除的感知自监督学习网络 (PSLNet)，它基于并行网络来去除噪音和水印，在结构信息和像素增强方面改善图像水印移除效果，通过综合实验验证了该方法在与卷积神经网络 (CNNs) 相比的嘈杂图像水印移除方面的高效性。

感知自供训练网络用于噪声图像水印去除

Perceptive self-supervised learning network for noisy image watermark  removal

Burst super-resolution (BurstSR) aims at reconstructing a high-resolution
(HR) image from a sequence of low-resolution (LR) and noisy images, which is
conducive to enhancing the imaging effects of smartphones with limited sensors.
The main challenge of BurstSR is to effectively combine the complementary
information from input frames, while existing methods still struggle with it.
In this paper, we suggest fusing cues frame-by-frame with an efficient and
flexible recurrent network. In particular, we emphasize the role of the
base-frame and utilize it as a key prompt to guide the knowledge acquisition
from other frames in every recurrence. Moreover, we introduce an implicit
weighting loss to improve the model's flexibility in facing input frames with
variable numbers. Extensive experiments on both synthetic and real-world
datasets demonstrate that our method achieves better results than
state-of-the-art ones. Codes and pre-trained models are available at
this https URL

本文提出了一种逐帧融合的、高效灵活的循环网络，以底帧为关键提示，并引入隐式加权损失，用于促进从具有可变数量的输入帧中获取知识，以实现从序列低分辨率和噪声图像重建高分辨率图像的目标。我们在合成和真实数据集上进行了广泛实验，结果表明，我们的方法优于当前最先进的方法。