Text Attribute Noise Variation based Multi-Scale Image Analysis

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S.Bhargav Kumar
S.Bhargav Kumar
σ
Dr. M.Ashok
Dr. M.Ashok
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Dr.T.Bhaskara Reddy
Dr.T.Bhaskara Reddy
α Jawaharlal Nehru Technological University, Hyderabad

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Text Attribute Noise Variation based Multi-Scale Image Analysis

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Abstract

For image reconstruction, the particular constant quantity of the received image should be same as original image with the given analysis. This paper implements an analysis algorithm, where the particular constant quantity are analysed via image texture leaning with an appropriate variable variation’s. In this paper, a three level decomposed multi-wavelet (3LMW)-based multi-scale image noise variation analysis scheme for image text attribute noise variation (TANV) and image analysis algorithm is proposed and the determination of the optimal 3LMW basis with respect to the proposed scheme is also discussed. The proposed method is applied to image noise variation analysis, and the experimental results validated its generality and effectiveness in multi-style image noise variation analysis.

References

5 Cites in Article
  1. Xuejie Qin,Yee-Hong Yang (2004). Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval.
  2. Eric Balster,Y Zheng,R Ewing (2005). Feature-based wavelet shrinkage algorithm for image denoising.
  3. Andrea Baraldi,Lorenzo Bruzzone,Palma Blonda (2006). A Multiscale Expectation-Maximization Semisupervised Classifier Suitable for Badly Posed Image Classification.
  4. A Jose,Luis Guerrero-Col´on,Javier Mancera,Portilla (2007). Image Restoration Using pace-Variant Gaussian Scale Mixtures in Overcomplete Pyramids.
  5. Manya Afonso,José Bioucas-Dias,Mário Figueiredo (2010). Fast Image Recovery Using Variable Splitting and Constrained Optimization.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

S.Bhargav Kumar. 2015. \u201cText Attribute Noise Variation based Multi-Scale Image Analysis\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 15 (GJCST Volume 15 Issue F2): .

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Issue Cover
GJCST Volume 15 Issue F2
Pg. 13- 19
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification: I.3.3
Version of record

v1.2

Issue date

July 15, 2015

Language
en
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For image reconstruction, the particular constant quantity of the received image should be same as original image with the given analysis. This paper implements an analysis algorithm, where the particular constant quantity are analysed via image texture leaning with an appropriate variable variation’s. In this paper, a three level decomposed multi-wavelet (3LMW)-based multi-scale image noise variation analysis scheme for image text attribute noise variation (TANV) and image analysis algorithm is proposed and the determination of the optimal 3LMW basis with respect to the proposed scheme is also discussed. The proposed method is applied to image noise variation analysis, and the experimental results validated its generality and effectiveness in multi-style image noise variation analysis.

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Text Attribute Noise Variation based Multi-Scale Image Analysis

Dr. M.Ashok
Dr. M.Ashok
Dr.T.Bhaskara Reddy
Dr.T.Bhaskara Reddy
S.Bhargav Kumar
S.Bhargav Kumar Jawaharlal Nehru Technological University, Hyderabad

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