Quality processing of microarray image data through image inpainting and texture synthesis

2005 ◽  
Author(s):  
P. O'Neill ◽  
G.D. Magoulas ◽  
Xiaohui Liu
Keyword(s):  
Texture Synthesis ◽  
Image Inpainting ◽  
Image Data ◽  
Microarray Image

Fullbore Image Construction of Imaging Logging Based on the Improved Texture Inpainting Method

2014 ◽  
Vol 571-572 ◽  
pp. 825-828
Author(s):  
Xiang Zhang ◽  
Jun Hua Wang ◽  
Xiao Ling Xiao
Keyword(s):  
Local Search ◽  
Texture Synthesis ◽  
Synthesis Method ◽  
Image Inpainting ◽  
Search Method ◽  
Local Search Method ◽  
Synthesis Algorithm ◽  
Image Construction ◽  
Tv Model ◽  
Improved Algorithm

The image inpainting method based on CriminiciA’s algorithm is slowly complete the image for large blank area. An improved algorithm based on the classic texture synthesis algorithm for image inpainting is proposed for imaging logging inpainting, which is used to generate the fullbore image. Two schemes, the local search method and priority calculation with TV model, are employed in the improved texture synthesis method. Some examples were given to demonstrate the effectiveness of the proposed algorithm on dealing with fullbore image construction with large blank area and raising efficiency obviously.

scholarly journals Image Inpainting Technique Based on Smart Terminal: A Case Study in CPS Ancient Image Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69837-69847 ◽  
Author(s):  
Yu Weng ◽  
Haiwen Zhou ◽  
Jian Wan
Keyword(s):  
Image Inpainting ◽  
Image Data

Web-based tissue microarray image data analysis: Initial validation testing through prostate cancer Gleason grading

2001 ◽  
Vol 32 (4) ◽  
pp. 417-427 ◽  
Author(s):  
G.Steven Bova ◽  
Giovanni Parmigiani ◽  
Jonathan I. Epstein ◽  
Thomas Wheeler ◽  
Neil R. Mucci ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Tissue Microarray ◽  
Image Data ◽  
Web Based ◽  
Gleason Grading ◽  
Initial Validation ◽  
Microarray Image ◽  
Validation Testing

scholarly journals Image Inpainting Methods Evaluation and Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Raluca Vreja ◽  
Remus Brad
Keyword(s):  
Partial Derivative ◽  
Texture Synthesis ◽  
Image Inpainting ◽  
Digital Processing ◽  
Analytical Comparison ◽  
Image Structure ◽  
Hybrid Techniques ◽  
Methods And Techniques

With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira’s and Hadhoud’s algorithms, which are performing well on images with natural defects.

A Novel Image Inpainting Method Based on Sparse Decomposition

2015 ◽  
Vol 738-739 ◽  
pp. 573-577
Author(s):  
Xuan Zhu ◽  
Xu Feng Zhang ◽  
Qiu Ju Li ◽  
Ji Yao Tao ◽  
Ben Yuan Li
Keyword(s):  
Texture Synthesis ◽  
Synthesis Method ◽  
Image Inpainting ◽  
Discrete Wavelet ◽  
Sparse Decomposition ◽  
Component Decomposition ◽  
Morphological Component ◽  
Curvature Driven ◽  
Different Characteristics ◽  
Redundant Discrete Wavelet Transform

Redundant Discrete Wavelet Transform (RDWT) and Wavelet Atomic Transform (WAT) are proposed in this paper as a couple of dictionaries to get the structure and texture basing on morphological component decomposition. Then, basing on the fact that the structure and texture have different characteristics, in this paper we use curvature driven diffusion model and Criminisi texture synthesis method to inpaint the structure and texture respectively. At last, compound the inpainted structure and texture and get the inpainting result. The experiment results show the new method can not only decompose the image very well, but also inpaint the image with strong and fairing edge, complete and clear texture .This method shows better results in image inpainting compared to the classical ones.

AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

2010 ◽  
Vol 24 (04) ◽  
pp. 581-600 ◽  
Author(s):  
NING DENG ◽  
HUILONG DUAN
Keyword(s):  
Power Spectrum ◽  
Image Data ◽  
Microarray Experiment ◽  
Microarray Image ◽  
Image Projection ◽  
Microarray Datasets ◽  
Microarray Analyses ◽  
Microarray Image Processing ◽  
Spectrum Model ◽  
Experimental Comparisons

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

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