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21,179 results for “image enhancement”
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments (dataset)
<p>These files are the simulation data used to create the figures illustrated in the journal paper with the same title which has been accepted for publication as a regular paper in IEEE Transactions on Computational Imaging. Each filename indicates the figure number associated with the file. These files are text files. The column structure of each file is described in the README file.</p>
Volumetric imaging of cellular dynamics with deep learning enhanced bioluminescence microscopy
<p>The low photon emission of known luciferases, currently limit their widespread use as contrast agents in live cell microscopy because they demand long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy as an alternative for fluorescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. Here, we leverage a versatile training data set for deep learning based bioluminescence microscopy including paired images of noisy and ground thruth fluorescence data of body wall muscle labeled <em>Caenorhabditis elegans</em> animals. These data include light-field images and their ground truth reconstructions for training a CNN for fast light fiel deconvolution.</p>
Reproduction Package (VirtualBox Image) for the POPL 2024 Article `Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations`
<p>This is the artifact for the ACM PACMPL article <i>Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations</i>. We provide our artifact as an easy-to-use VirtualBox image, which contains the benchmarks, our tools for bit-vector synthesis, and the scripts for generating the results showcased in the paper.</p>
Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics
<p>Dataset of the work entitled "Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics".</p>
Draft version of poposed images and plotting code of manuscript: Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>This documentation provides a detailed description of the folder structure and image contents uploaded to the website. It aims to help users understand the purpose and organization of the files. The corresponding manuscript is titled,<strong><em> Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite</em></strong>.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system
<p>In computer vision, images or objects are recognised by machine going through two phases<br> shown in the figure 4. First the system is trained with features extracted from sample images in<br> training stage then they are tested on input images in testing stage. The performance of the classifier<br> depends on features extracted from the image. This research work is carried out in three different<br> experiments, first experiment is performed on data set containing the original images of sixteen<br> categories, noisy images are classified in second experiment, and third experiment detects the type<br> of noise affected the image followed by filtering through appropriate filter, then filtered images are<br> classified. The performance of each of the experiment is measured with two approaches. First<br> approach extracts the statistical texture features of the whole image, and the original image of size<br> 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical<br> texture features discussed in second section are extracted from each of the block producing 96<br> features from each of the images are used for training and testing stage.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 7. Performance in experiment 3 for both approaches
<p>Classification of noisy images starts with detection of type of noise followed by appropriate<br> filtering operation. Then similar approaches are followed for feature extraction as discussed in<br> above experiments.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Feed Forward Back propagation Neural Network
<p>This BPNN provides a computationally efficient<br> method for changing the weights in feed forward network, with differentiable activation function<br> units, to learn a training set of input-output data. Being a gradient descent method it minimizes the<br> total squared error of the output computed by the net. The aim is to train the network to achieve a<br> balance between the ability to respond correctly to the input patterns that are used for training and<br> the ability to provide good response to the input that are similar. A typical back propagation<br> network of input layer, one hidden layer and output layer is shown in figure 4.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 6. Performance in experiment 2 for both approaches
<p>Second experiment is performed on noisy data set with 50 images each class for training of<br> feed forward neural network, and 100 images each class are used for testing phase. This experiment<br> is also carried out with two approaches as discussed in first experiment.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 5. Performance in experiment 1 for both approaches
<p>The first experiment is carried out on data set containing 16 class and 50 images of each<br> class for training and 100 images of each class for testing. Initially the original images are resized to<br> 128X128 pixels and six texture features are extracted from 800 training images in the first<br> approach. It produces a feature matrix of size 6X800. Similarly 6X1600 feature matrix is produced<br> for testing stage. Then a neural classifier is designed with ten hidden layers and 16 output layers.<br> Feature matrix of training images are used to train the neural network. After that they are tested on feature matrix of test image. In second approach, the original images are divided into 16 blocks of<br> size 32X32 pixel each block. So it produces 96 features (16 blocksX6 features) for each image and<br> feature matrix of 96X800 for training image and 96X1600 feature matrix for testing samples.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image
<p>The Wiener filter is the mean square error-optimal stationary linear filter for images<br> degraded by additive noise and blurring. It removes the additive noise and inverts the blurring<br> simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is<br> based on a stochastic framework.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'
<p>Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'</p>
Imaging data from "Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD"
<p>3D 20ms, 3D 500ms and 2D dCas9 raw videos, localisation, tracking and trajectory analysis data</p> <p>From 'Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD" (2021). Biorxiv. https://doi.org/10.1101/2020.04.03.003178</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
<p>The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed both spontaneous and light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.