Image Processing
Structural analysis of minuscule height objects holds paramount significance in fields such as industrial manufacturing and medical testing. Currently, 3D reconstruction method based on shape from focus (SFF) has emerged as an efficacious approach for acquiring submicron-level height change information. A novel multi-field SFF(MF-SFF) framework incorporates pulse-controlled continuous acquisition methods and parallel chain processing (PCP) strategy, effectively addressing challenges associated with minuscule height objects.
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OCD description. Cell lines A172 and U251: human glioblastoma; MCF7: human breast cancer; MRC5: human lung fibroblast; SCC25: human squamous cell carcinoma. Cultivation condition CTR: cells belonging to the control group - without the addition of chemotherapy; TMZ: cells treated with 50 μM temozolomide in some cultivation step.
Split
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LDRText is a large-scale and diverse dataset that suitable for scene text image super-resolution and recognition tasks
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This dataset is derived from Sentinel-2 satellite imagery.
The main goal is to employ this dataset to train and classify images into two classes: with trees, and without trees.
The structure of the dataset is 2 folders named: "tree" (images containing trees) and "no-trees" (images without presence of trees).
Each folder contains 5200 images of this type.
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With the fast growth of deep learning, trainable frameworks have been presented to restore hazy images. However, the capability of most existing learning-based methods is limited since the parameters learned in an end-
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In international contexts, natural scenes may include text in multiple languages. Especially, Latin and Arabic scene character image dataset is essential for training models to accurately detect and recognize text regions within real-world images. This is crucial for applications such as text translation, image search, content analysis, and autonomous vehicles that need to interpret text in different languages.
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This is a data set to show the advantage of the method in this paper, the data set has two kinds of data: the multi-sine phase-shifting images and comparison method images, the images are all modulated for different targets, such as: the white ceramic cuboid, The half of white ping pong ball and no targets. In this data set, the “3phase.bmp” are the images for the method in this paper, the ”2plus1.bmp” are the images for all kinds of two phase-shifting methods, where R layer image is average intensity image, G layer and B layer images are phase-shifting images.
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The MalariaSD dataset encompasses diverse stages and classes of malaria parasites, including Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, and Plasmodium ovale, categorized into four phases: ring, schizont, trophozoite, and gametocyte.
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This is the dataset used in the paper "Application of improved lightweight network and Choquet fuzzy ensemble technology for soybean disease identification". This data set contains 6 types of soybean disease leaves collected from Xiangyang Farm, Nengjiang Farm and Jiusan Farm of Northeast Agricultural University in Heilongjiang Province from early June to late September 2019. All images are collected in natural scenes. A total of 1620 disease images of soybean leaves were collected.
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