A New Fusion Algorithm for MRI and Color Images Based on Mutual Information in Wavelet Domain

2008 ◽  
Author(s):  
Renbin Hou ◽  
Xiaolin Tian ◽  
Yankui Sun ◽  
Zesheng Tang
Keyword(s):  
Mutual Information ◽  
Color Images ◽  
Wavelet Domain ◽  
Fusion Algorithm

scholarly journals Image Denoising in Wavelet Domain with Filtering and Thresholding

2018 ◽  
Vol 7 (3.34) ◽  
pp. 327
Author(s):  
K Sumathi ◽  
Ch Hima Bindu
Keyword(s):  
Mutual Information ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Color Images ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Noisy Images

In this paper, the proposed method is implemented for removal of salt & pepper and Gaussian noise of black & white & color images toacquire the quality output. In this work initially wavelet coefficients are extracted for noisy images. Later apply denoise filteringtechnique on the high transform sub bands of noisy images (either color/ B & W) using new laplacian filters with 4 directions. Finallythreshold of an image is generated to extract denoisy coefficients. At last inverse of above subband coefficients can give denoise imagefor further processing. The proposed method is verified against various B & W/color images and it gives a better PSNR (Peak Signal toNoise Ratio) & MI (Mutual Information). These values are compared with different noise densities and analyzed visually.

A New Robust Reversible Blind Watermarking in Wavelet-Domain for Color Images

2016 ◽  
Vol 16 (02) ◽  
pp. 1650006 ◽  
Author(s):  
P. Manimehalai ◽  
P. Arockia Jansi Rani
Keyword(s):  
Clustering Algorithm ◽  
Color Image ◽  
Reversible Watermarking ◽  
Color Images ◽  
Blind Watermarking ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Recovered Image ◽  
Host Image

Reversible watermarking methods are used for copyright protection and are able to recover the host image without distortion. Robust reversible watermarking technique should resist against intentional and unintentional image processing attacks. Robust reversible watermarking techniques should have three features namely imperceptibility, reversibility and robustness. In this paper, it is proposed to develop a new robust reversible blind watermarking for color images based on histogram construction of the wavelet coefficients constructed from the cover image. In the proposed approach, the red component of a host color image is decomposed into wavelet coefficients. Motivated by the excellent spatio-frequency localization properties of wavelets, this technique is proposed in the wavelet domain. The pixels are adjusted before watermark embedding such that both overflow and underflow of pixels during embedding is avoided and image is recovered without distortion. Based on histogram construction and the local sensitivity of Human Visual System (HVS) in wavelet domain, the watermark is embedded. For watermark extraction without host image, k-means clustering algorithm is proposed. The experimental results show that the proposed technique has good performance in terms of reversibility and robustness with the high quality of the watermarked image. The PSNR value of the recovered image is around 48[Formula: see text]dB which proves that the quality of the recovered image is not degraded.

scholarly journals Image fusion algorithm based on block-DCT in wavelet domain

2011 ◽  
Vol 60 (11) ◽  
pp. 114205
Author(s):  
Gan Tian ◽  
Feng Shao-Tong ◽  
Nie Shou-Ping ◽  
Zhu Zhu-Qing
Keyword(s):  
Image Fusion ◽  
Wavelet Domain ◽  
Fusion Algorithm ◽  
Block Dct

Multi-Modal Medical Image Fusion Algorithm Based On Spatial Frequency Motivated PA-PCNN In NSST Domain

2020 ◽  
Vol 16 ◽  
Author(s):  
Vanitha Kamarthi ◽  
D. Satyanarayana ◽  
M.N. Giri Prasad
Keyword(s):  
Mutual Information ◽  
Spatial Frequency ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Fusion Algorithm ◽  
Frequency Bands ◽  
Edge Strength

Background: Image fusion has been grown as an effectual method in diseases related diagnosis schemes. Methods: In this paper, a new method for combining multimodal medical images using spatial frequency motivated parameter-adaptive PCNN (SF-PAPCNN) is suggested. The multi-modal images are disintegrated into frequency bands by using decomposition NSST. The coefficients of low frequency bands are selected using maximum rule. The coefficients of high frequency bands are combined by SF-PAPCNN. Results: The fused medical images is obtained by applying INSST to above coefficients. Conclusion: The quality metrics such as entropy ENT, fusion symmetry FS, deviation STD, mutual information QMI and edge strength QAB/F are used to validate the efficacy of suggested scheme.

scholarly journals An Objective Non-Reference Metric Based on Arimoto Entropy for Assessing the Quality of Fused Images

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 879 ◽  
Author(s):  
Bicao Li ◽  
Runchuan Li ◽  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Zongmin Wang
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Principal Component ◽  
Laplacian Pyramid ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Normalized Mutual Information ◽  
Guided Filtering ◽  
Reference Measure

In the technologies, increasing attention is being paid to image fusion; nevertheless, how to objectively assess the quality of fused images and the performance of different fusion algorithms is of significance. In this paper, we propose a novel objective non-reference measure for evaluating image fusion. This metric employs the properties of Arimoto entropy, which is a generalization of Shannon entropy, measuring the amount of information that the fusion image contains about two input images. Preliminary experiments on multi-focus images and multi-modal images using the average fusion algorithm, contrast pyramid, principal component analysis, laplacian pyramid, guided filtering and discrete cosine transform have been implemented. In addition, a comparison has been conducted with other relevant quality metrics of image fusion such as mutual information, normalized mutual information, Tsallis divergence and the Petrovic measure. The experimental results illustrate that our presented metric correlates better with the subjective criteria of these fused images.

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