Brightness preserving based on singular value decomposition for image contrast enhancement

Optik ◽  
2015 ◽  
Vol 126 (7-8) ◽  
pp. 799-803 ◽  
Author(s):  
Randa Atta ◽  
Rabab Farouk Abdel-Kader
Keyword(s):  
Singular Value Decomposition ◽  
Contrast Enhancement ◽  
Singular Value ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Value Decomposition

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.

Shock filter based image super-resolution using dual-tree complex wavelet transform and singular value decomposition

2016 ◽  
Vol 35 (3) ◽  
Author(s):  
suryanarayana gunnam ◽  
Ravindra Dhuli
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Design Methodology ◽  
Super Resolution ◽  
Singular Value ◽  
Image Contrast ◽  
Complex Wavelet Transform ◽  
Shock Filter ◽  
Image Super Resolution ◽  
Value Decomposition

Purpose The purpose of this paper is to present an improved wavelet based approach in single image super resolution (SISR). The proposed method generates high resolution (HR) images by preserving the image contrast and edges simultaneously. Design/methodology/approach Covariance based interpolation algorithm is employed to obtain an initial estimate of the unknown HR image. The proposed method preserves the image contrast, by applying singular value decomposition (SVD) based correction on the dual-tree complex wavelet transform (DT-CWT) coefficients. In addition, the dual operating mode diffusion based shock filter (DBSF) ensures noise mitigation and edge preservation. Findings Experimental results on various test images reveal superiority of the proposed method over the existing SISR techniques in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and visual quality. Originality/value With SVD based correction, the proposed method preserves the image contrast and also the DBSF operation helps to achieve sharper edges.

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