An Improved Fingerprint Image Synthesis Algorithm Based on Gray Value and Its Fast DSP Implementation

2008 ◽  
Author(s):  
Dongxiang Chi
Keyword(s):  
Image Synthesis ◽  
Fingerprint Image ◽  
Synthesis Algorithm ◽  
Dsp Implementation

An Improved Texture Image Synthesis Algorithm

2016 ◽  
Vol 53 (12) ◽  
pp. 121001 ◽  
Author(s):  
谭永前 Tan Yongqian ◽  
曾凡菊 Zeng Fanju ◽  
岳莉 Yue Li ◽  
吴位巍 Wu Weiwei
Keyword(s):  
Image Synthesis ◽  
Texture Image ◽  
Synthesis Algorithm

Variable-viewpoint stereo image synthesis algorithm based on Unity3D

2018 ◽  
Vol 33 (8) ◽  
pp. 697-702
Author(s):  
李春江 LI Chun-jiang ◽  
赵悟翔 ZHAO Wu-xiang ◽  
王琼华 WANG Qiong-hua
Keyword(s):  
Image Synthesis ◽  
Stereo Image ◽  
Synthesis Algorithm

scholarly journals HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM

2016 ◽  
Vol XLI-B7 ◽  
pp. 661-665 ◽  
Author(s):  
Nguyen Tien Hoang ◽  
Katsuaki Koike
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Landsat Imagery ◽  
Image Synthesis ◽  
Hyperspectral Remote Sensing ◽  
High Spectral Resolution ◽  
Synthesis Algorithm ◽  
Spatial Coverage ◽  
Hyperion Data

Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.

scholarly journals Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Adib Keikhosravi ◽  
Bin Li ◽  
Yuming Liu ◽  
Matthew W. Conklin ◽  
Agnes G. Loeffler ◽  
...  
Keyword(s):  
Second Harmonic ◽  
Image Synthesis ◽  
Fibrillar Collagen ◽  
Clinical Workflow ◽  
Synthesis Algorithm ◽  
Different Types ◽  
Stromal Collagen ◽  
Diagnostic Work ◽  
Second Harmonic Generation Imaging ◽  
Picrosirius Red

AbstractThe importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

scholarly journals A Full-Aperture Image Synthesis Method for the Rotating Rectangular Aperture System Using Fourier Spectrum Restoration

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 522
Author(s):  
Guomian Lv ◽  
Hao Xu ◽  
Huajun Feng ◽  
Zhihai Xu ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Fourier Spectrum ◽  
Synthesis Method ◽  
Image Synthesis ◽  
Objective Evaluation ◽  
The Novel ◽  
Point Spread Functions ◽  
Synthesis Algorithm ◽  
Rectangular Aperture ◽  
Point Spread ◽  
Resolution Imaging

The novel rotating rectangular aperture (RRA) system provides a good solution for space-based, large-aperture, high-resolution imaging tasks. Its imaging quality depends largely on the image synthesis algorithm, and the mainstream multi-frame deblurring approach is sophisticated and time-consuming. In this paper, we propose a novel full-aperture image synthesis algorithm for the RRA system, based on Fourier spectrum restoration. First, a numerical simulation model is established to analyze the RRA system’s characteristics and obtain the point spread functions (PSFs) rapidly. Then, each image is used iteratively to calculate the increment size and update the final restored Fourier spectrum. Both the simulation’s results and the practical experiment’s results show that our algorithm performs well in terms of objective evaluation and time consumption.

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