Image Processing
Reconsructed images (video sequences) and results for the paper: "Adaptive block compressive sensing for distributed video coding"
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This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).
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Level 2 data set. Averaging over multiple measurements were performed to reduce electronic noise and jitter
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The dataset contains rash images of 11 different disease states. Images of normal skin are also included in the dataset.
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This dataset is accompanying the manuscript "Lossless archiving view arrays from plenoptic cameras when camera sensor images are available" by Ioan Tabus and Emanuele Palma, published in ISSCS 2021 in July 2021. It is also supporting part of the work carried out in “Lossless Compression of Plenoptic Camera Sensor Images” by Ioan Tabus and Emanuele Palma, published in IEEE Access in February 2021. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.
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This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
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Cautionary traffic signs are of immense significance to traffic safety. In this study, a robust and optimal real-time approach to recognize the Indian Cautionary Traffic Signs(ICTS) is proposed. ICTS are all triangles with a white backdrop, a red border, and a black pattern. A dataset of 34,000 real-time images has been acquired under various environmental conditions and categorized into 40 distinct classes. Pre-processing techniques are used to transform RGB images to Gray-scale images and enhance contrast in images for superior performance.
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Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.
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