Image contrast enhancement using singular value decomposition for gray level images

2011 ◽  
Author(s):  
P. Sivakumar ◽  
K. Maguesway ◽  
M. Rajaram
Keyword(s):  
Singular Value Decomposition ◽  
Contrast Enhancement ◽  
Singular Value ◽  
Image Contrast ◽  
Gray Level ◽  
Image Contrast Enhancement ◽  
Value Decomposition ◽  
Gray Level Images

Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images

Optik ◽  
2012 ◽  
Vol 123 (6) ◽  
pp. 511-520 ◽  
Author(s):  
Ming Zeng ◽  
Youfu Li ◽  
Qinghao Meng ◽  
Ting Yang ◽  
Jian Liu
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Gray Level ◽  
X Ray ◽  
Image Contrast Enhancement ◽  
Level Information

scholarly journals An approach for image Contrast and Resolution Enhancement based Wavelet Transform by incorporating Cuckoo Search and SVD algorithms

2019 ◽  
Vol 8 (11) ◽  
pp. 24858-24868
Author(s):  
Sandeepa K S ◽  
Basavaraj N Jagadale ◽  
J S Bhat
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Search Algorithm ◽  
Resolution Enhancement ◽  
Cuckoo Search ◽  
Singular Value ◽  
Image Contrast ◽  
Discrete Wavelet ◽  
Maximum Value ◽  
Value Decomposition

Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. With the proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. The resolution of the image is enhanced by combining interpolated high-frequency sub-band and maximum value fusion images. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.

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