Directional Subbands Research Articles

An Adaptive Image Fusion Method Based on NSCT and AGA

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy, and directional expansion for images. When the NSCT is introduced to image fusion, more information for fusion can be obtained and the impacts of mis-registration on the fused results can also be reduced effectively. Therefore, the NSCT is more suitable for image fusion. Based on NSCT and adaptive Genetic Algorithm (AGA), this paper proposes a new adaptive fusion method. This method can optimize decomposition level of the NSCT, lowpass subband coefficients and bandpass directional subband coefficients of the fused image. Finally, the algorithm is tested and compared with existing algorithms. The results show that the algorithm can produce high-quality fused images rapidly.

Image Denoising Method with Adaptive Bayes Threshold in Nonsubsampled Contourlet Domain

In this paper, an improved image denoising method based on a nonlinear thresholding function with adaptive bayes threshold in Nonsubsampled Contourlet Transform (NSCT) domain. In overcoming the shortcomings of the same threshold, the noise deviation of the different sub-band are estimated based on the coefficients of different directional and level sub-bands in NSCT domain, and the thresholds of every sub-band is estimated by Bayesian threshold estimation method. After choosing the thresholds, a nonlinear thresholding function was chosen to overcome the shortcomings of the soft and the hard thresholding function. The simulation results show that the proposed method in this paper can remove Gaussian white noise more effectively, and get a higher PSNR value and keep image texture and detail information more clearly, which also has a better visual effect.

Contourlet hidden Markov Tree and clarity-saliency driven PCNN based remote sensing images fusion

In this paper a new Contourlet hidden Markov Tree (CHMT) and clarity-saliency driven Pulse Coupled Neural Network (PCNN) based fusion approach is proposed for remote sensing images fusion. Considering the failure of wavelet in representing the geometry of image edges and contours, we firstly use Contourlet to provide an efficient and flexible multiscale, multidirectional and anisotropy representation of remote sensing images, and then use CHMT model to describe the statistics of Contourlet coefficients, by taking the dependencies across scales, locations and directions into account. Because CHMT can span several adjacent directional subbands in the finer scales, which has similar statistical characteristic to scale, CHMT model can give more accurate description of images and has lower computational complexity than wavelet hidden Markov Tree (WHMT). Training the Contourlet coefficients of registered multisource images using Expectation Maximum (EM) algorithm, one can obtain the model parameters which is used to update the Contourlet coefficients. Low-frequency subbands are fused by the magnitude maximum rule. For the fusion of high-frequency directional subband, a PCNN is constructed based on the phenomena of synchronous pulse bursts in the animal visual cortexes, and the linking strength of each neuron in PCNN is determined by a new clarity-saliency measure of the subband images. New fire mapping images are obtained for each high-frequency subband taking part in the fusion. Some experiments are taken on some remote sensing images came from the USA Airborne Multisensor Pod System (AMPS) program, and the results show the superiorities of our proposed method to WHMT and Contourlets, both in subjective evaluation, implementation speed and some numerical guidelines.

A New Digital Watermarking Algorithm Based on DWT-DCT-RLC

The paper presents an imperceptible and robust digital watermarking algorithm using a combination of the DWT-DCT , which improves the robustness via using new embedding strategy. Apply 4-level DWT to the host image. Then, DCT transform of each directional middle frequencies DWT subband is computed; A binary watermarked logo is coded by Run-Length Code(RLC);The watermark bits are embedded in the coefficients of the DCT block based on the energy of each corresponding to coefficient in the highest level selected using the value of RLC. Performance evaluation show that the proposed scheme is invisible and robust against common image manipulations like JPEG compression, noise adding, sharpening, filtering.

Visual uniformity recognition of nonwovens with contourlet energy features and K-NN classifier

The surface uniformity recognition and inspection is an important and vital procedure of nonwoven production. Here, a novel approach for the uniformity recognition of nonwovens based on contourlet energy features and K-NN classifier is proposed. In this paper, the uniformity recognition based on contourlet transform and K-NN classifier is considered as a special case of pattern recognition problem that will be solved in two stages. In the first stage, the nonwoven images are decomposed with contourlet transform and then two energy features, norm-1 L1 and norm-2 L2 are calculated from contourlet coefficients of each bandpass directional subband. In the second stage, the extracted energy-based features are used to train and test K-NN classifier, and for comparison, the experimental results coming from different feature set, L1, L2 and L12 (the combinations of L1 and L2) are discussed. In experiment, when the nonwoven images are decomposed at level 3 using contourlet transform and 24 energy-based features, i.e., L12s, are used to train 1-NN classifier, the average recognition accuracy is 98.4 %, which is superior to the method based on wavelet energy-based features.

Multiscale illumination normalization for face recognition using dual-tree complex wavelet transform in logarithm domain

A new illumination normalization approach based on multiscale dual-tree complex wavelet transform (DT-CWT) is presented for variable lighting face recognition. In our method, low resolution wavelet coefficients are truncated to minimize variations under different lighting conditions. On the other hand, the coefficients in the directional subbands are used for extracting the invariant facial features. In order to reduce the halo artifacts, new conduction function is presented which can smooth the high-frequency illumination discontinuities while keeping most of the facial features unimpaired. The histogram remapping technique is employed on the normalized image which remaps the histogram into normal distribution and thus it can improve the recognition performance. Experimental results on the Yale B and CMU PIE databases show that the proposed method can achieve satisfactory performance for recognizing face images under large illumination variations.

Similarity-based multimodality image fusion with shiftable complex directional pyramid

For multimodality images, a novel fusion algorithm based on the shiftable complex directional pyramid transform (SCDPT) is proposed in this paper. As well, with the aid of the structural similarity (SSIM) index, a ‘similarity-based’ idea is employed to distinguish regions with ‘redundant’ or ‘complementary’ information between source imagers before the SCDPT coefficients are merged. A ‘weighted averaging’ scheme for regions with ‘redundant’ information and a ‘selecting’ scheme for regions with ‘complementary’ information are then employed, respectively. When merging the low-pass subband coefficients, the SSIM index in spatial domain (SP-SSIM) is employed as similarity measure, and three types of regions are thus determined. Especially, for regions with similar intensity values but different intensity changing directions between source images, a ‘selecting’ scheme based on gradient and energy is proposed. When merging the directional band-pass subband coefficients, the SSIM index in complex wavelet domain (CW-SSIM) is employed as similarity measure. With the CW-SSIM index, not only the magnitude information but also the phase information of SCDPT coefficients can be employed. Compared to the traditional energy matching (EM) index based fusion methods, the proposed method can better deal with ‘redundant’ and ‘complementary’ information of source images. In addition, because of the shift-invariance of the SCDPT and the CW-SSIM index, the proposed fusion algorithm performs well even if the input images are not well registered. Several sets of experimental results demonstrate the validity and feasibility of the proposed method in terms of both visual quality and objective evaluation.

A HYBRID FACE RECOGNITION APPROACH USING LOCAL FUSION OF COMPLEX DUAL-TREE WAVELET COEFFICIENTS AND RIDGELET TRANSFORM

In this paper, we propose novel face recognition method based on local appearance feature extraction using hybrid mode of local ridge-let and fused dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel dis-crete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of sub-bands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands and local Finite Ridgelet Transform (FRIT) coefficients of approxi-mate subbands of DT-CWT are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been success-fully applied to face recognition and achieved state-of-the-art per-formance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concate-nated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped paral-lelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of hybrid FRIT and fused DT-CWT coeffi-cients are used to describe the face image. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local hybrid FRIT and fused DT-CWT approach performs well on illumi-nation, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our pro-posed Hybrid Local FRIT and fused DT-CWT based method has a really low computational complexity.

Contourlet Transform Based Watermarking for Colour Images

This paper presents a contourlet transform based watermarking scheme for colour images. It also makes use of discrete cosine transform and singular value decomposition. Discrete cosine transform is applied on three colour components (Y, Cb and Cr), obtained from the colour image, to produce DCT coefficients. These coefficients are decomposed into directional subbands using 2- level contourlet transform. The watermark is also decomposed using contourlet transform. The fourth directional subbands of cover image and watermark as well are further subjected to Laplacian pyramid decomposition. As a result, the low pass subbands are obtained. The singular values of lowpass subband of watermark are embedded into singular values of lowpass subband of cover image. The experimental results reveal that the proposed watermarking scheme is robust against common image processing attacks. The results also demonstrate good imperceptibility.

The multiscale directional bilateral filter and its application to multisensor image fusion

In this paper, a novel multiscale geometrical analysis called the multiscale directional bilateral filter (MDBF) which introduces the nonsubsampled directional filter bank into the multiscale bilateral filter is proposed. Through combining the characteristic of preserving edge of the bilateral filter with the ability of capturing directional information of the directional filter bank, the MDBF can better represent the intrinsic geometrical structure of images. The MDBF, which is a multiscale, multidirectional and shift-invariant image decomposition scheme, is used to fuse multisensor images in this paper. The source images are first decomposed into the directional detail subbands and the approximation subbands via the MDBF. Then, the directional detail subbands and the approximation subbands are fused according to the given fusion rule, respectively. Finally, the inverse MDBF is applied to the fused subbands to obtain the fused image. Experimental results over visible and infrared images and medical images demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures.

Image Denoising Algorithm Based on Dyadic Contourlet Transform

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 it 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.

Image Denoising Algorithm Based on Dyadic Contourlet Transform

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.

DESIGN AND REALIZATION OF CIRCULAR CONTOURLET TRANSFORM

In this paper, circular contourlet transform (CCT) is proposed, designed and realized. As in the classical contourlet transform (CT), a double filter bank structure is also considered in this work but in different manners. A circularly-decomposed filter bank is first used to capture the points of discontinuities in the image edges, and then followed by a directional filter bank to obtain smoothed contours. The resulting CCT contains a critically sampled filter bank that decomposes images into any power of two's number of directional subbands at multiple scales. The designed CCT is realized by 2-D lattice allpass sections with separable and non-separable 2-D functions of z1 and z2. The resulting structure preserves both modularity and regularity properties which are suitable for VLSI implementations. Objectively, the performances of the realized CCT are tested and proved to be better than the classical CT in detail image preservation. The resulting subband images also indicate the superiority of the proposed CCT. Keywords: Circular contourlet transform, Contourlet transform, Laplacian pyramid, Directional filter bank, 2-D lattice allpass sections, Multiresolution (multiscale & multidirection) analysis.

A Contourlet-Based Image Watermarking Scheme with High Resistance to Removal and Geometrical Attacks

We propose a new nonblind multiresolution watermarking method for still images based on the contourlet transform (CT). In our approach, the watermark is a grayscale image which is embedded into the highest frequency subband of the host image in its contourlet domain. We demonstrate that in comparison to other methods, this method enables us to embed more amounts of data into the directional subbands of the host image without degrading its perceptibility. The experimental results show robustness against several common watermarking attacks such as compression, adding noise, filtering, and geometrical transformations. Since the proposed approach can embed considerable payload, while providing good perceptual transparency and resistance to many attacks, it is a suitable algorithm for fingerprinting applications.

STUDY ON THE ENHANCEMENT AND DENOISING OF MAMMOGRAMS

A new algorithm for mammograms enhancement and denoising based on multiscale geometric analysis(MGA) was proposed.Firstly,mammograms were decomposed into different scales and directional subbands by using nonsubsampled contourlet transform(NSCT).After modeling the coefficients of each directional subbands and using generalized gaussian mixture model(GGMM) according to the statistical property of the transform coefficients,they were categorized into strong edges,weak edges and noise by Bayesian classifier.A nonlinear mapping function was designed to enhance and suppress the different coefficients adaptively so as to obtain a good enhancement result with significant features according to the property of every subband coefficient.Finally,the mammograms were obtained by reconstructing the original maps with the modified coefficients of NSCT.Experimental results illustrate that the proposed approach is practicable and robust.

Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform

In this paper, an unsupervised change-detection method for multitemporal satellite images is proposed. The algorithm exploits the inherent multiscale structure of the dual-tree complex wavelet transform (DT-CWT) to individually decompose each input image into one low-pass subband and six directional high-pass subbands at each scale. To avoid illumination variation issue possibly incurred in the low-pass subband, only the DT-CWT coefficient difference resulted from the six high-pass subbands of the two satellite images under comparison is analyzed in order to decide whether each subband pixel intensity has incurred a change. Such a binary decision is based on an unsupervised thresholding derived from a mixture statistical model, with a goal of minimizing the total error probability of change detection. The binary change-detection mask is thus formed for each subband, and all the produced subband masks are merged by using both the intrascale fusion and the interscale fusion to yield the final change-detection mask. For conducting the performance evaluation of change detection, the proposed DT-CWT-based unsupervised change-detection method is exploited for both the noise-free and the noisy images. Extensive simulation results clearly show that the proposed algorithm not only consistently provides more accurate detection of small changes but also demonstrates attractive robustness against noise interference under various noise types and noise levels.

Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain

In order to alleviate the effect of illumination variations on face recognition, a novel face recognition algorithm based on illumination invariant in nonsubsampled contourlet transform (NSCT) domain is proposed. The algorithm first performs logarithm transform on original face images under various illumination conditions, which changes multiplicative illumination model into an additive one. Then NSCT is used to decompose the logarithm transformed images. After that, adaptive NormalShrink is applied to each directional subband of NSCT for illumination invariant extraction. Experimental results on the Yale B, the extended Yale and the CMU PIE face databases show that the proposed algorithm can effectively alleviate the effect of illumination on face recognition.

LOCAL STATISTICAL FEATURES OF DUAL TREE COMPLEX WAVELET TRANSFORM BASED FACE RECOGNITION WITH SINGLE SAMPLE

In this paper, we propose novel face recognition method based on local appearance feature extraction using dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel discrete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The local DT-CWT coefficients are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been successfully applied to face recognition and achieved state-of-the-art performance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concatenated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped parallelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of complex wavelet coefficients are used to describe the face image.Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local DT-CWT approach performs well on illumination, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our proposed Local DT-CWT based method has a really low computational complexity.

Local fusion of complex dual-tree wavelet coefficients based face recognition for single sample problem

In this paper, we propose a novel face recognition method for the one sample problem. This approach is based on local appearance feature extraction using directional multiresolution decomposition offered by dual tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel discrete wavelet transforms (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. The 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands are used to extract the facial features which improve the face recognition with small sample size in relatively short computation time. The local features based methods have been successfully applied to face recognition and achieved state-of-the-art performance. Normally for most of the local appearance based methods, the facial features are extracted from several local regions and concatenated into an enhanced feature vector as a face descriptor. In this approach we divide the fused face representation into several (m×m) non-overlapped parallelogram blocks instead of square or rectangular blocks. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local fusion of DT-CWT approach performs well on illumination, expression and perspective variant faces with a single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our proposed Local fusion of DT-CWT based method has a really low computational complexity.

Robust iris recognition algorithm for non-cooperative environment

Non-cooperative iris recognition system results in blurred and noisy images that can degrade the performance of the system. This paper proposes a novel feature extraction algorithm for non-cooperative environment. The proposed iris subdivision method reduces the effect of noise due to non-cooperative behaviour in an authentication system. Optimal projection analysis is proposed to obtain the rotation invariant features from the directional subband coefficients of the normalised image block. A fusion algorithm combines the matching scores from individual subimages to reduce the false rejection rate. The performance of the proposed method is compared with other algorithms on UBIRIS iris image database.