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Research Article 


Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique

Dr. Raman Chadha, Rajesh Kumar.

Abstract
- Image reconstruction is a method in which image data is reconstructed from input parcel data. The partial input data may include the horizontal projection, vertical projections, horizontal and vertical projections, diagonal projection, and anti-diagonal projection data. The image reconstruction algorithms are used to reconstruct the partially lost data or corrupted information as of the remaining traces of the image data. The binary image matrices are constructed of the 1-bit image pixels, which contains only two combinations and can hold the value of 1 and 0. The binary images are also classified as the black and white images, as two-bit images can only show either black or white color represented by 1 or 0 values in the binary image matrices. There are several binary image reconstruction methods already proposed for the binary image reform from some kind of input traces or the projection data to reconstruct the whole binary matrix. The initial image reconstruction solution is returned once the maximum iteration is reached or the horizontal and vertical combination different equals zero during the reconstruction by estimating the binary combinations produced by the projected mock-up solution. The heritable programming have been utilized in the second stage image matrix reconstruction, which utilizes the primary phases of image reconstruction based upon the genetic reproduction, which includes the crossover, mutation, and fitness for the finalization of the binary image matrix reconstruction solution. Various kind of experiments is being carried out through which a binary image can be reconstructed. Higher reconstruction accuracy has been recorded by the proposed model which is in the structure of the modernization fault. The time complexity has been evaluated in the proposed model evaluation against the existing models. The study talks about the proposed model for image reconstruction accuracy which has achieved a good position over the existing model.

Key words: RGB, MATLAB,HV, OCR,CMYK.


 
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Pubmed Style

Dr. Raman Chadha, Rajesh Kumar. Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. EEO. 2021; 20(5): 5473-5484. doi:10.17051/ilkonline.2021.05.615


Web Style

Dr. Raman Chadha, Rajesh Kumar. Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. http://ilkogretim-online.org//?mno=74616 [Access: April 19, 2021]. doi:10.17051/ilkonline.2021.05.615


AMA (American Medical Association) Style

Dr. Raman Chadha, Rajesh Kumar. Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. EEO. 2021; 20(5): 5473-5484. doi:10.17051/ilkonline.2021.05.615



Vancouver/ICMJE Style

Dr. Raman Chadha, Rajesh Kumar. Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. EEO. (2021), [cited April 19, 2021]; 20(5): 5473-5484. doi:10.17051/ilkonline.2021.05.615



Harvard Style

Dr. Raman Chadha, Rajesh Kumar (2021) Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. EEO, 20 (5), 5473-5484. doi:10.17051/ilkonline.2021.05.615



Turabian Style

Dr. Raman Chadha, Rajesh Kumar. 2021. Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. Elementary Education Online, 20 (5), 5473-5484. doi:10.17051/ilkonline.2021.05.615



Chicago Style

Dr. Raman Chadha, Rajesh Kumar. "Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique." Elementary Education Online 20 (2021), 5473-5484. doi:10.17051/ilkonline.2021.05.615



MLA (The Modern Language Association) Style

Dr. Raman Chadha, Rajesh Kumar. "Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique." Elementary Education Online 20.5 (2021), 5473-5484. Print. doi:10.17051/ilkonline.2021.05.615



APA (American Psychological Association) Style

Dr. Raman Chadha, Rajesh Kumar (2021) Bi-Faceted Binary Image Modernization Using Matrix Data Optimization Technique. Elementary Education Online, 20 (5), 5473-5484. doi:10.17051/ilkonline.2021.05.615








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