Image Processing
The dataset is a validation dataset for low-light image enhancement and noise reduction tasks. The dataset contains triples of images: low-light images, target images and low-light enhanced images. We used this dataset to generate results for the manuscript "Adaptive Guided Upsampling for Low-light Image Enhancement" submitted to IEEE ACCESS for review. The dataset allows other researchers to work our material.
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Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment.
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We employed Hexacopter unmanned aerial vehicle (UAV) equipped with the SPECIM FX17E hyperspectral camera to implement ultra-low-altitude flight aerial photography missions with atmospheric correction processing. We collected three hyperspectral images and combined them into three data pairs, which exhibit varying degrees of spectral shift. Among them, a hyperspectral image including six types of ground objects was collected in Changsha at 4:00 pm on September 27, 2021, with sunny weather and a flight altitude of 30m, named CSSunny.
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The Nematode Detection Dataset is a comprehensive collection of 1,368 high-quality microscope images specifically curated for the advancement of agricultural pest management through machine learning. This dataset has been meticulously assembled to aid in the detection, identification, and analysis of four key types of nematodes that are critical to global agriculture: Meloidogyne (Root-knot nematodes), Globodera pallida (Potato cyst nematodes), Pratylenchus (Root-lesion nematodes), and Ditylenchus (Stem nematodes).
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Aboveground biomass (AGB) is a vital indicator for studying the carbon sink in forest ecosystems. Semi-arid forests harbor substantial carbon storage but received little attention as high spatial-temporal heterogeneity. This study assessed the performance of different data sources (annual monthly time-series radar: Sentinel-1 (S1), annual monthly time-series optical: Sentinel-2 (S2), and single-temporal airborne LiDAR) and seven prediction approaches to map AGB in the semi-arid forests at the border between Gansu and Qinghai provinces in China.
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The study included 50 epilepsy patients undergoing long-term video-EEG monitoring at the Epilepsy Center of Guangdong 999 Brain Hospital. The inclusion criteria for patients were as follows: (1) VEEG reports confirming definite epileptic seizures, (2) complete video data containing both seizure and non-seizure periods, (3) no intentional interference during patient seizures, and no occlusion of the patient, such as patients were covered by quilts.
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The dataset comprises image files of size 640 x 480 pixels for various grit sizes of Abrasive sheets. The data collected is raw. It can be used for analysis, which requires images for surface roughness. The dataset consists of a total of 8 different classes of surface coarseness. There are seven classes viz. P80, P120, P150, P220, P320, P400, P600 as per FEPA (Federation of European Producers of Abrasives) numbering system and one class viz. 60 as per ANSI (American National Standards Institute) standards numbering system for abrasive sheets.
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301 Views
This is the relevant data in "Monocular Homography Estimation and Positioning Method for the Spatial-Temporal Distribution of Vehicle Loads Identification".
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This dataset includes various gauge blocks of different heights at different positions. This includes two sets of data with no targets and different measurement heights. Each data consists of 16 phase-shifting images and their corresponding Gray code images.
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64 Views
This dataset includes various gauge blocks of different heights at different positions. This includes two sets of data with no targets and different measurement heights. Each data consists of 16 phase-shifting images and their corresponding Gray code images.
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9 Views