MSVD based image watermarking in NSCT domain

Cellular automata based color image watermarking using non sub sampled contourlet transform (NSCT) and singular value decomposition (SVD) is presented in this paper. Game of life (GOL) cellular automata is used to scramble binary watermark so that no one can recover the watermark without secret scrambling keys. Because of the salient features like multiscale, multidirection and shift invariance of NSCT, 2-level NSCT is applied to red, green and blue component of RGB cover image. Most of the perceptual content of an image focuses on low frequency subband of NSCT and the singular values taken from low frequency subband have certain stability. So a modest change in the singular values taken from low frequency subband of NSCT is applicable. In this proposed scheme, each singular value of scrambled binary watermark is embedded in fixed location of singular value matrix of low frequency subbands of NSCT of red, green and blue components. The scheme offers security, imperceptibility and robustness against various image processing and geometric attacks like Gaussian white noise, salt and pepper noise, median filtering, JPEG compression, cropping and rotation. The proposed scheme is compared with existing watermarking schemes in terms of Peak Signal to Noise ratio (PSNR), Normalized Correlation (NC)and provides satisfactory performance than other schemes.

In this paper, a novel image watermarking scheme using an encrypted kinoform in the hybrid nonsubsampled contourlet transform (NSCT) and singular value decomposition (SVD) domain is presented. In the scheme, a watermark is firstly transformed into an encrypted kinoform with fractional Fourier transform and Gerchberg-Saxton algorithm. Then the host image is decomposed with NSCT and the low frequency sub-band is divided into nonoverlapping blocks. SVD is applied to every block. The kinoform is embedded into the low frequency NSCT coefficients by modifying the largest singular value of each block. To withstand geometric attacks, the geometric distortion parameters of the attacked watermarked image are computed first by the Zernike moment-based geometric correction method in the detection process. Then the mark kinoform is extracted from the recovered image. In comparison with other method, the proposed watermarking scheme offers better performances for imperceptibility. Experimental results demonstrate that the proposed image watermarking is not only invisible but also robust against a variety of attacks including scaling, rotation, flipping, noise addition, filtering, JPEG compression, etc.

The suppression of random noise in seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) of seismic data is much lower which bring great difficulty to seismic data processing and interpretation. In this paper, we propose a new seismic random noise suppression approach based on the non-subsampled contourlet transform (NSCT). Firstly, the noisy seismic data is decomposed into different sub-bands of frequency and orientation responses using NSCT. Then, we use an appropriate thresholding operator to denoise the sub-bands of NSCT coefficients. Finally, we perform the inverse NSCT transform on the denoised NSCT components to reconstruct the denoised seismic data. We use synthetic data examples to demonstrate the superior performance of the proposed approach over the wavelet transform based threshold denoising method.

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

Image compression is preferred for less storage space, sending image on the internet and often for medical imaging in health industry, technical drawings or comics. Because of wide popularity of sharing media in internet, compression plays a major role in the watermarking algorithm. This paper presents a compression technique through image watermarking algorithm based on Non-subsampled Contourlet Transform (NSCT) and Singular Value Decomposition (SVD). NSCT and SVD both are explored for image watermarking. For image compression, only Singular Value Decomposition is used. The NSCT can give more accurate coefficients of the edges and contours in image which is shift invariant. After getting the coefficients from NSCT decomposition SVD is applied to break the matrix in a convenient way so that image compression can be done without losing meaningful data. The compressed singular values of NSCT decomposed coefficients of cover image are added with the compressed singular values of NSCT decomposed coefficient of gray key image. This helps to improve the performance of the compression using watermarking method. In terms of PSNR and compression ratio, the performance of this scheme is analyzed. The proposed algorithm is robust and suitable for copyright protection and data transmission against compression applications.

Aiming at solving the problems of part edges loss and blurred texture information in the image fusion process of infrared light image and visible light image, an improved algorithm based on regional texture smoothness and gradation mutation degree for the infrared light and visible light image fusion is proposed. Firstly, Non-subsampled Contourlet transform(NSCT) is adopted. The multi-scale and multi-direction sparse decomposition is performed for the infrared light source image and visible light source image. Then the low-frequency component reflecting the image approximate contents and the every bandpass directional subband components reflecting the image detail characteristics are obtained respectively. The fusion method based on the gradation mutation weighted average for the low-frequency subband component is adopted, and the fusion method based on the composition of regional texture smoothness and Laplace energy sum for the high-frequency subband is adopted. Finally, the fusion image is reconstructed through the NSCT reversed transform. The experimental results show that in the condition of reserving the source image information, the fusion image clarity is raised, the detail information and brightness contrast are enhanced by this fusion method.

The nonsubsampled contourlet transform (NSCT) is a powerful and versatile tool that allows a multiresolution and directional representation to be achieved. In this paper, we propose an extension of two despeckling algorithms, proposed to restore SAR images and based on the undecimated separable wavelet transform, to work into the NSCT domain. The signal is modeled as affected by a multiplicative noise. The noise-free NSCT coefficients are estimated from the observed ones according to either the maximum-a-posteriori (MAP) or the linear minimum mean square error (LMMSE) criterion. The results show that the proposed restoration algorithms highly benefit from the fact of working into a multiresolution and multidirectional domain.

Infrared and visible image fusion can obtain an integrated image containing obvious object information and high spatial resolution background information. Therefore, combining the characteristics of infrared and visible images to obtain the fused image has important research significance. In this paper, an effective fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. The method is based on the application of a modulated pulse-coupled neural network fusion (PCNN) strategy and an energy attribute fusion strategy in the NSCT domain. First, NSCT is used to decompose the input original image into low frequency sub-images and high frequency sub-images. Then, the high frequency sub-images are fused via a multi-level morphological gradient (MLMG) domain PCNN and the low frequency sub-images are fused via the energy attribute fusion strategy. Finally, the fused sub-images are reconstructed by inverse NSCT. Experimental results demonstrate that the proposed algorithm has a better fusion performance in both subjective evaluation and objective evaluation.

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

Image fusion combines information from multiple images of the same scene to obtain a composite image which is more suitable for further image processing tasks. This study presented an image fusion scheme based on the modified dual pulse coupled neural network (PCNN) in non-subsampled contourlet transform (NSCT) domain. NSCT can overcome the lack of shift invariance in contourlet transform. Original images were decomposed to obtain the coefficients of low-frequency subbands and high-frequency subbands. In this fusion scheme, a new sum-modified Laplacian of the low-frequency subband image, which represents the edge-feature of the low-frequency subband image in NSCT domain, is presented and input to motivate modified dual PCNN. For fusion of high-frequency subband coefficients, spatial frequency will be used as the gradient features of images to motivate dual channel PCNN and to overcome Gibbs phenomena. Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.

In multi-focus image fusion, accurately detecting and extracting focused regions remains a key challenge. Some existing methods suffer from misjudgment of focus areas, resulting in incorrect focus information or the unintended retention of blurred regions in the fused image. To address these issues, this paper proposes a novel multi-focus image fusion method that leverages a dual-channel Rybak neural network combined with consistency verification in the nonsubsampled contourlet transform (NSCT) domain. Specifically, the high-frequency sub-bands produced by NSCT decomposition are processed using the dual-channel Rybak neural network and a consistency verification strategy, allowing for more accurate extraction and integration of salient details. Meanwhile, the low-frequency sub-bands are fused using a simple averaging approach to preserve the overall structure and brightness information. The effectiveness of the proposed method has been thoroughly evaluated through comprehensive qualitative and quantitative experiments conducted on three widely used public datasets: Lytro, MFFW, and MFI-WHU. Experimental results show that our method consistently outperforms several state-of-the-art image fusion techniques, including both traditional algorithms and deep learning-based approaches, in terms of visual quality and objective performance metrics (QAB/F, QCB, QE, QFMI, QMI, QMSE, QNCIE, QNMI, QP, and QPSNR). These results clearly demonstrate the robustness and superiority of the proposed fusion framework in handling multi-focus image fusion tasks.

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

Image Fusion Algorithm Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

ABSTRACTIn order to improve speckle noise denoising of block matching and 3D filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3D denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5 dB. The MLFE-BM3D method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.

Multimodal medical image fusion technique is an important and essential coalescing technique for the medical images with different modalities. The fused medical image carries more useful information than two or more relevant individual medical images of modality. To enhance, preserve edge and feature information and to remove noise of the source images a novel medical image fusion method has been developed with multiscale edge preserving decomposition L0 smoothing with sparse representation (SR) in nonsubsampled contourlet transform (NSCT) domain. The NSCT‐based image fusion method provides richer information in the spatial and spectral domains simultaneously. In this method, initially, L0 gradient smoothing filter is applied in two different modal clinical images separately to decompose the source images into two layers such as low frequency layer (LFL) and high frequency layer (HFL) which preserves the information and improves quality. To maintain the curve edges and the energy of the source medical images, the LFLs of different modal images are combined by using the NSCT ‐ SR fusion rule also to protect the detailed information of input medical images and reduce redundant information, the HFLs are combined by max‐absolute combination rule. By combining the reconstructed LFL and HFL, the resultant fused image is obtained. The experimental results show that the proposed work can provide better results than current methodologies in terms of both visual consistency and quantitative analysis.