一个小型版本的数据集,它由代表 160 个场景实例的160 个图像对(噪声和ground-truth)组成。 Papers Abdelrahman Abdelhamed, Lin S., Brown M. S. "A High-Quality Denoising Dataset for Smartphone Cameras", IEEE Computer Vision and Pattern
SIDD-small,全称 Smartphone Image Denoising Dataset一个小型版本的图像降噪数据集,它由代表 160 个场景实例的160 个图像对(噪声和ground-truth)组成。该数据集被用于来评估一些降噪算法。
Abdelrahman Abdelhamed, Lin S., Brown M. S. "A High-Quality Denoising Dataset for Smartphone Cameras", IEEE Computer Vision and Pattern Recognition (CVPR), June 2018.
Abdelrahman Abdelhamed, Timofte R., Brown M. S., et al. "NTIRE 2019 Challenge on Real Image Denoising: Methods and Results", IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), June 2019.
A simple camera pipeline for rendering raw-RGB images into sRGB.
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SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images.
SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images.
智能手机图像去噪数据集(SIDD)使用5个具有代表性的智能手机摄像头,从10个场景中提取约30000个噪声图像,并生成它们真实的场景图像。
Introduction目前效果出色的深度去噪方法大都采用监督学习的方法,需要采集输入-输出图像对(noisy/noise-free images pairs)建立训练数据集。数据集的建立是关键的任务。数据集的质量将直接决定去噪结果的质量。如何获取尽量多场景的图像数据,如何获得高质量的参考图像(ground truth),是目前研究的热点。The State of ArtsMe...
关键点检测手部姿势关键点检测数据集数据集下载链接: ZV数据集由序列构成。在每个序列中,您都可以找到组成它的帧。一个帧由4个彩色图像、4组投影在每个图像平面中的2D关节、4个边界框、1组Lea…
包含全国水文监测点名称,行政区,流域,水系,编码,经度,纬度,河流,站类代码,站点类型,地址,时间,数据来源:全国雨水情信息
地表水水质监测系统监视和测定水体中污染物的种类、各类污染物的浓度及变化趋势,评价水质状况, 提供地表水水质历史记录
荧光显微镜使现代生物学取得了巨大的发展。由于其固有的微弱信号,荧光显微镜不仅比摄影噪声大得多,而且还呈现出泊松-高斯噪声,其中泊松噪声或散粒噪声是主要的噪声源。为了获得干净的荧光显微镜图像,非常需要有专门设计用于对荧光显微镜图像进行降噪的有效降噪算法和数据集。虽然存在这样的算法,但没有这样的数据集可用。在本文中,我们通过构建专用于泊松-高斯去噪的数据集 - 荧光显微镜去噪 (FMD) 数据集来填补
Description在 TinyPerson 中有 1610 个标记图像和 759 个未标记图像(两者主要来自同一视频集),总共有 72651 个注释。tiny person detection taskFor tiny person detection task, the rules of evaluation on test set are given as follows:we treat