无参考图像质量评价，深度学习，生成对抗网络，adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA).

2021-2022年使用

空气质量监测 我什么时候应该打开窗户呼吸新鲜空气？ 这就是答案！ 该存储库提供了一个现成的可安装应用程序，用于通过和覆盆子pi进行空气质量监测。 设置 你会需要： 树莓派 Flash已建立图片 如果您只是想这样做，则可以安装来自的最新映像，并将其。 从源安装 您可能希望使用来简化安装。 如果已经设置了Raspberry Pi，则可以从以下来源安装该软件包： # get the source git clone git@github.com:randombenj/air-quality-monitoring.git cd air-quality-monitoring/qualitair # install dependencies poetry install # run it! poetry shell cd .. python -m qualitair 桌面通知 如果要在何时打开

RELIABILITY and QUALITY MANAGEMENT

matlab的egde源代码盲图质量工具箱 该项目是Matlab中用于盲图质量评估的算法的集合。 暗示： IQVG需要一些Libsvm-Mex文件，这些文件在此处可用：不要忘记将库添加到路径中（请参见computeQualityMetrics.m）。 DIIVINE需要Matlab的特殊版本，可在此处获得：不要忘记将库添加到路径中（请参见computeQualityMetrics.m）。 根据您的环境设置，可能需要修改所有libsvm可执行文件的路径（请参见IQVG.m，brisquescore.m） 品质保证局 Salvador Gabarda和GabrielCristóbal，“通过各向异性进行盲眼图像质量评估”，J。 Soc。 是。 A 24，B42-B51（2007年） 碧琪 AK Moorthy和AC Bovik，“构造盲通用质量指标的模块化框架”，提交给IEEE Signal Processing Letters（2009年）。 AK Moorthy和AC Bovik，“ BIQI软件版本”，2009年。 盲人2 MA Saad和AC Bovik，“盲图像质量评估：DCT域

基于统计特征的Quality Phrase挖掘方法软件工程分析.docx

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, the acquisition of image quality scores has several limitations: 1) scores are not precise, because subjects are usually uncertain about which score most precisely represents the perceptual quality of a given image; 2) subjective judgments of quality may be biased by image content; 3) the quality scales between different distortion categories are inconsistent, because images corrupted by different types of distortion are evaluated independently in subjective experiments; and 4) it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting sufficient images associated with different kinds of distortion that have diverse levels of degradation, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as “the quality of image Ia is better than that of image Ib” for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance

水质 全国各流域水质数据 数据数据环保部公开数据（每日12时自动更新） 更新：2020-11-1接口变更

High-quality 3D shape measurement using saturated fringe patterns

安卓应用-健康医疗