Self-supervised Image Enhancement Network: Training with Low Light Images Only 现有的图像增强数据集都是通过合成或者调整曝光时间得到的,但存在两个问题:①如何确保预先训练的网络可以用于不同设备、不同场景和不同照明条件下收集的图像,而不是构建新的训练数据集。②如何确定用于监督的正常光图像是最好的,因为相...
为了解决这个问题,我们提出了一种自监督的图像特定原型探索(SIPE),它由图像特定原型探索(IPE)和通用特定一致性(GSC)损失组成。具体来说,IPE为每个图像定制原型以捕获完整的区域,形成了我们的ImageSpecific CAM (IS-CAM),这是通过两个连续步骤实现的。在此基础上,通过GSC构建了通用CAM与特定is -CAM的一致性,进一步...
在MagicLens的训练数据生成过程中,PaLI和PaLM2各自发挥重要的作用。PaLI作为一个多语言视觉语言模型,被用来生成图像的文本描述。而PaLM2则作为一个大型语言模型,利用图像对的元数据,生成能够连接两张图像的开放式文本指令。通过PaLI和PaLM2的配合,MagicLens的训练数据集包含了3670万个(三元组)样本,即图像、文本指令和...
We, also demonstrate that our model learns to generate textual description from image data as well as images from textual data both qualitatively and quantitatively.doi:10.48550/arXiv.2112.04928Das, Anindya SundarSaha, Sriparna
Image denoising plays very crucial role in applications which include photography/videography, Security-Surveillance, Medical Image analysis, Astronomy, Aerial photography, Satellite Imagery, and many more. The results of denoising also affect the accuracy of later tasks of image segmentation, object ...
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset can serve as a strong benchmark for better evaluating raw image denoising. Code and dataset will be released at this ...
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our...
Zeng, X., Xiang, H., Yu, L.et al.Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework.Nat Mach Intell4, 1004–1016 (2022). https://doi.org/10.1038/s42256-022-00557-6 ...
论文中让我印象最深的就是SE,注意力机制和上采样层,还有就是对SSIM这个损失函数的理解更清晰一点了。 遗憾的就是不知道最后一个损失函数的计算方式,还有就是没有源码。 参考 [1] Self-supervised feature adaption for infrared and visible image fusion...
Y. Huang, J. Lyu, P. Cheng, R. Tam and X. Tang, "SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading", IEEE Journal of Biomedical and Health Informatics (JBHI), 2024. [arxiv] [JBHI]DatasetThe datasets used in this work are listed as follows:Pre...