scholarly journals Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yingkun Hou ◽  
Xiaobo Qu ◽  
Guanghai Liu ◽  
Seong-Whan Lee ◽  
Dinggang Shen
Keyword(s):  
Image Enhancement ◽  
Image Filtering ◽  
Block Matching ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Haar Transform ◽  
Image Edge ◽  
Representation Method ◽  
Guided Image Filtering

In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

Image Denoising Algorithm Based on Dyadic Contourlet Transform

2010 ◽  
Vol 40-41 ◽  
pp. 591-597 ◽  
Author(s):  
Fan Hui ◽  
Yong Liang Wang ◽  
Jin Jiang Li
Keyword(s):  
Image Denoising ◽  
Wavelet Denoising ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Visual Images ◽  
Translation Invariance ◽  
Noise Interference ◽  
Filter Group ◽  
Image Edge ◽  
Adaptive Denoising

This paper constructs a dyadic non-subsampled Contourlet transform for denoising on the image, the transformation has more directional subband, using the non-subsampled filter group for decompositing of direction, so has the translation invariance, eliminated image distortion from Contourlet transform’s lack of translation invariance. Non-subsampled filter reduces noise interference and data redundancy. Using the feature of NSCT translation invariance, multiresolution, multi-direction, and can according to the energy of NSCT in all directions and in all scale, adaptive denoising threshold. Experimental results show that compared to wavelet denoising and traditional Contourlet denoising, the method achieves a higher PSNR value, while preserving image edge details, can effectively reduce the Gibbs distortion, improve visual images.

Adaptive image enhancement using nonsubsampled contourlet transform domain histogram matching

2014 ◽  
Vol 12 (s2) ◽  
pp. S21002-321005
Author(s):  
Yan Zhou Yan Zhou ◽  
Qingwu Li Qingwu Li ◽  
Guanying Huo Guanying Huo
Keyword(s):  
Image Enhancement ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform ◽  
Histogram Matching

Beta Chaotic Map Based Image Encryption Using Genetic Algorithm

2018 ◽  
Vol 28 (11) ◽  
pp. 1850132 ◽  
Author(s):  
Manjit Kaur ◽  
Vijay Kumar
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Optimal Parameter ◽  
Fitness Function ◽  
Chaotic Map ◽  
Input Image ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Computational Speed

In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique.

Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence for Face Recognition

2013 ◽  
Vol 433-435 ◽  
pp. 405-411
Author(s):  
Rong Bing Huang ◽  
Xiao Qun Liu
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Feature Maps ◽  
Face Expression ◽  
Face Features ◽  
The Face ◽  
Representation Method ◽  
Pattern Histogram

In order to alleviate the effect of illumination variations and improve the face recognition rate, this paper proposes a novel non-statistics based face representation method, which is called Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence (CS-LNBPHS). This method first applies NSCT to decompose a face image, and obtains NSCT coefficients in different scales and various orientations. Then, CS-LBP operator is used to get CS-LBP feature maps from NSCT coefficients. After that, feature maps are respectively divided into several blocks, the concatenated histogram, which are calculated over each block, are used as the face features. Experimental results on YaleB, ORL face databases show the validity of the proposed approach especially for illumination, face expression and position.

Sign in / Sign up

Export Citation Format

Share Document