Ghost Image Correction in CSTAR Photometry

2013 ◽  
Vol 125 (930) ◽  
pp. 1015-1020 ◽  
Author(s):  
Zeyang Meng ◽  
Xu Zhou ◽  
Hui Zhang ◽  
Jilin Zhou ◽  
Songhu Wang ◽  
...  
Keyword(s):  
Image Correction ◽  
Ghost Image

A Study on the Advancement of Character Recognition Using Image Correction

2020 ◽  
Vol 20 ◽  
pp. 225-245
Author(s):  
Hye Won Lee ◽  
Min Je Kim ◽  
Ju Chan Park ◽  
Gui Young Son ◽  
Mi Young Lee
Keyword(s):  
Character Recognition ◽  
Image Correction

Multiview Image Correction for Visually Equivalent Light Field 3D Display

2019 ◽  
pp. 1058
Author(s):  
Takasuke Nagai ◽  
Munekazu Date ◽  
Shinya Shimizu ◽  
Hideaki Kimata
Keyword(s):  
Light Field ◽  
Image Correction ◽  
3D Display

LED Display Image Correction Based on Computer Simulation

2012 ◽  
Vol 182-183 ◽  
pp. 1751-1755
Author(s):  
Xi Feng Zheng ◽  
Feng Chang
Keyword(s):  
Computer Simulation ◽  
Correction Method ◽  
Experimental Results ◽  
Image Correction ◽  
The Third

For the purposes of correcting the LED display image, a method based on computer simulation is proposed. First, the development of the LED display panel is introduced. Second, analyze the causes of the problem which image in LED display panel has serious high non-uniformity, and introduce the existed correction techniques which are used to reduce the non-uniformity of LED display image. Simultaneously, point out the ground for shortcomings of these techniques. Third, describe the principle of correction method based on computer simulation detail from two steps, which are the luminous collection and luminous copulation. Forth, describe the realization steps of this method in accordance with the third step. Finally, this method is supplied in a LED display panel, whose resolution is 640×480. Experimental results show that this method is able to reduce the non-uniformity of images from 11.06% to 0.98%..

scholarly journals Recovery of photoacoustic images based on accurate ultrasound positioning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yinhao Pan ◽  
Ningbo Chen ◽  
Liangjian Liu ◽  
Chengbo Liu ◽  
Zhiqiang Xu ◽  
...  
Keyword(s):  
Biological Tissue ◽  
Large Field ◽  
Photoacoustic Microscopy ◽  
Label Free ◽  
Imaging Data ◽  
Motor Speed ◽  
Image Correction ◽  
Microscopy Imaging ◽  
Scanning Method

AbstractPhotoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

Quantitative OCT image correction using Fermat's principle and mapping arrays

2002 ◽  
Author(s):  
Volker Westphal ◽  
Sunita Radhakrishnan ◽  
Andrew M. Rollins ◽  
Joseph A. Izatt
Keyword(s):  
Image Correction ◽  
Fermat’S Principle ◽  
Fermat's Principle ◽  
Mapping Arrays ◽  
Oct Image

scholarly journals Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Enrique Fernández-Rodicio ◽  
Víctor González-Pacheco ◽  
José Carlos Castillo ◽  
Álvaro Castro-González ◽  
María Malfaz ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Vertical Plane ◽  
Vertical Surface ◽  
Human Robot Interaction ◽  
Image Correction ◽  
Robot Interaction ◽  
Kinect Camera ◽  
Fitting Algorithm ◽  
The Right ◽  
Large Groups

Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

Three-dimensional auto-stereoscopic animated image with a long viewing distance using high-precision image correction

2009 ◽  
Author(s):  
Takehito Teraguchi ◽  
Hiromasa Yamashita ◽  
Ken Masamune ◽  
Takeyoshi Dohi ◽  
Hongen Liao
Keyword(s):  
High Precision ◽  
Three Dimensional ◽  
Viewing Distance ◽  
Image Correction

scholarly journals Color Image Correction of Art Paintings Based on Artists' Color Features and Illuminant Conversion

2013 ◽  
Vol 37 (2_3) ◽  
pp. 114-125
Author(s):  
Yoshie Imai ◽  
Ryoichi Saito ◽  
Takahiko Horiuchi ◽  
Shoji Tominaga
Keyword(s):  
Color Image ◽  
Image Correction ◽  
Color Features
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