Histogram Based Locality Preserving Contrast Enhancement
₹5,310.00
Histogram Based Locality Preserving Contrast Enhancement
Platform : Matlab
Delivery : One Working Day
Support : Online Demo ( 2 Hours)
100 in stock
Description
ABSTRACT
Histogram equalization (HE), a simple contrast enhancement (CE) method, tends to show excessive enhancement and gives unnatural artifacts on images with high peaks in their histograms. Histogram-based CE methods have been proposed in order to overcome the drawback of HE, however, they do not always give good enhancement results. In this letter, a histogram-based locality-preserving CE method is proposed. The proposed method is formulated as an optimization problem to preserve localities of the histogram for performing image CE. The locality-preserving property makes the histogram shape of the enhanced image to be similar to that of the original image. Experimental results show that the proposed histogram-based method gives output images with graceful CE on which existing methods give unnatural results.
EXISTING METHOD
- Histogram Equalization(HE)
- Brightness Preserving Bi-HE (BBHE)
- Minimum Mean Brightness Error BHE (MMBEBHE)
- Brightness Preserving Dynamic HE(BPDHE)
- Cascaded Multistep Binomial Filtering HE (CMBFHE)
- Recursively Separated And Weighted Histogram Equalization (RSWHE)
DRAWBACKS
- Image Contrast is Very Less
- Computational Complexity is Very High
- Computationally Expensive is More
PROPOSED METHOD
- Histogram-Based Locality- Preserving
BLOCK DIAGRAM
METHODOLOGIES
- Median Filter Process
- Histogram-Based Locality- Preserving
- Pixel NN Based Contract Enhancement
- Parameter Analysis
ADVANTAGES
- Image Contrast or Visibility is Very High
- Computational Complexity is Very Less
- Computationally Expensive is Less
APPLICATIONS
- Satellite Image
- Bio-Medical
- Digital Photograph (Multimedia)
SOFTWARE REQUIREMENT
- MATLAB 7.8 or above versions
RESULTS
REFERENCES
[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ: Pearson Education, 2010.
[2] Y. T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consumer Electron., vol. 43, no. 1, pp. 1–8, Feb. 1997.
[3] S.-D. Chen and A. R. Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” IEEE Trans. Consumer Electron., vol. 49, no. 4, pp. 1310–1319, Nov. 2003.
[4] H. Ibrahim and N. S. P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron., vol. 53, no. 4, pp. 1752–1758, Nov. 2007.
[5] S.-D. Chen and A. Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Trans. Consumer Electron., vol. 49, no. 4, pp. 1301–1309, Nov. 2003.
[6] D. Menotti, L. Najman, J. Facon, and A. A. Araujo, “Multi-histogram equalization methods for contrast enhancement and brightness preserving,” IEEE Trans. Consumer Electron., vol. 53, no. 3, pp. 1186–1194, Aug. 2007.
Additional information
Weight | 0.000000 kg |
---|
Reviews
There are no reviews yet.