Nonlocal Mumford–Shah and Ambrosio–Tortorelli Variational Models for Color Image Restoration

2015 ◽  
pp. 235-266
Keyword(s):  
Image Restoration ◽  
Color Image ◽  
Variational Models

scholarly journals Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

2019 ◽  
Vol 29 (1) ◽  
pp. 1480-1495
Author(s):  
D. Khalandar Basha ◽  
T. Venkateswarlu
Keyword(s):  
Linear Regression ◽  
Image Restoration ◽  
Color Image ◽  
Estimation Method ◽  
Similarity Index ◽  
Block Matching ◽  
Support Vector ◽  
Image Block ◽  
Testing Stage

Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.

Color Image Restoration Using Morphological Detectors and Adaptive Filter

2009 ◽  
pp. 381-388 ◽  
Author(s):  
Anita Sahoo ◽  
Rohal Suchi ◽  
Neha Khan ◽  
Pooja Pandey ◽  
Mudita Srivastava
Keyword(s):  
Image Restoration ◽  
Adaptive Filter ◽  
Color Image

Color image restoration and inpainting via multi-channel total curvature

2018 ◽  
Vol 61 ◽  
pp. 280-299 ◽  
Author(s):  
Lu Tan ◽  
Wanquan Liu ◽  
Zhenkuan Pan
Keyword(s):  
Image Restoration ◽  
Color Image ◽  
Total Curvature

scholarly journals Weighted Tensor Nuclear Norm Minimization for Color Image Restoration

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88768-88776 ◽  
Author(s):  
Kaito Hosono ◽  
Shunsuke Ono ◽  
Takamichi Miyata
Keyword(s):  
Image Restoration ◽  
Color Image ◽  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Norm Minimization ◽  
Tensor Nuclear Norm
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