Bandpass Subband Research Articles

A bio-inspired two-scale complementarity evaluation method for image fusion

A bio-inspired two-scale image complementarity evaluation method is proposed. This novel multi-scale method provides a promising alternative for the performance assessment of image fusion algorithms. Moreover, it can also be used to compare and analyze the multi-scale difference of raw images. Two metrics are presented and used to assess the complementarity of fusion images in non-subsampled contourlet transform (NSCT) domains: visual saliency differences (VSDs) at the coarse scales and detail similarities (DSs) at the fine scales. Visual attention mechanism (VAM)-based saliency maps are combined with NSCT low-pass subbands to compute the VSDs, and linear correlation and contrast consistency-based DSs are compared in NSCT band-pass subbands. Five main multi-scale transform (MST)-based fusion algorithms were compared by using 30 groups of raw images that consist of four types of fusion images. Effects of NSCT filters and decomposition levels on evaluation results are discussed in detail. Furthermore, a group of color multi-exposure fusion images were also taken as examples to evaluate the complementarity of raw images. Experimental results demonstrate the effectiveness of the proposed method, especially for MST-based image fusion algorithms.

Design of complex adaptive multiresolution directional filter bank and application to pansharpening

This paper proposes a new 2-D transform design, namely complex adaptive multiresolution directional filter bank, to represent the spatial orientation features of an input image adaptively. The proposed design is completely shift invariant and represents the input image by one low-pass and multiscale N directional band-pass subbands. Here, N represents estimated number of dominant directions present in the input image. Our design consists of two main filter bank stages. A fix partitioned complex-valued directional filter bank (CDFB) is at the core of the design followed by a novel partition filter bank stage. Fine partitioning of the CDFB subbands is used to get the adaptive nature of the proposed transform. The partitioning decision is made based on the directional significance of range of CDFB subband angle selectivity in the input image. Partition filter bank stage which nonuniformly partitions the CDFB subbands provides total N dominant direction selective subbands. Local orientation map of the input image is used to determine the dominant directions and hence N. For better sparsity properties, we design the multiresolution stage with filters having high vanishing moments and better frequency selectivity. Applicability of the proposed adaptive design is shown for pansharpening of multispectral images. Our proposed pansharpening approach is evaluated on images captured using QuickBird and IKONOS-2 satellites. Results obtained using the proposed approach on these datasets show considerable improvements in qualitative as well as quantitative evaluations when compared to state-of-the-art pansharpening approaches including transform-based methods.

Salient object detection fusing global and local information based on nonsubsampled contourlet transform.

The nonsubsampled contourlet transform (NSCT) has properties of multiresolution, localization, directionality, and anisotropy. The directionality property permits it to resolve intrinsic directional features that characterize the analyzed image. In this paper, we present a bottom-up salient object detection approach fusing global and local information based on NSCT. Images are first decomposed by applying NSCT. The coefficients of bandpass subbands are categorized and optimized accordingly to get better representation. Then feature maps are obtained by performing the inverse NSCT on these optimized coefficients. The global and local saliency maps are generated from these feature maps. Global saliency is obtained by utilizing the likelihood of features, and local saliency is measured by calculating the local self-information. In the end, the final saliency map is computed by fusing the global and local saliency maps together. Experimental results on MSRA 10K demonstrate the effectiveness and promising performance of our proposed method.

Multifocus image fusion scheme based on the multiscale curvature in nonsubsampled contourlet transform domain

An efficient multifocus image fusion scheme in nonsubsampled contourlet transform (NSCT) domain is proposed. Based on the property of optical imaging and the theory of defocused image, we present a selection principle for lowpass frequency coefficients and also investigate the connection between a low-frequency image and the defocused image. Generally, the NSCT algorithm decomposes detail image information indwells in different scales and different directions in the bandpass subband coefficient. In order to correctly pick out the prefused bandpass directional coefficients, we introduce multiscale curvature, which not only inherits the advantages of windows with different sizes, but also correctly recognizes the focused pixels from source images, and then develop a new fusion scheme of the bandpass subband coefficients. The fused image can be obtained by inverse NSCT with the different fused coefficients. Several multifocus image fusion methods are compared with the proposed scheme. The experimental results clearly indicate the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation.

Fusion of Infrared and Visible Images Based on Pulse Coupled Neural Network and Nonsubsampled Contourlet Transform

This paper presents a new fusion algorithm which can effectively solve the problem that has unobvious infrared target and low contrast in infrared and visible image fusion. This paper's innovation point is its fusion rule. Other algo- rithms' fusion rules usually use pulse coupled neural network (PCNN) and region characteristics to select low frequency or bandpass subband coefficients. The proposed algorithm innovatively applies improved PCNN and region characteris- tics to the selection of both low frequency and bandpasssubband coefficients in nonsubsampled contourled transform (NSCT) domain. First, the subband coefficients of original image are obtained by NSCT. Then, the decomposed subband coefficients are processed by using PCNN, whose fire mapping images are obtained. The method of region standard de- viation isused to choose the fusion coefficients of fire mapping image, which satisfies to get more image information in low frequency part. For bandpass subband coefficients' fire mapping image, the method based on region energy is adopted for the fusion coefficients, which makes the bandpass part captures more energy. Finally, the fused image can be obtained by inverse transform of NSCT. Compared with typical wavelet-based, NSCT-based, NSCT-PCNN based fusion algorithms, experiment shows that the new proposed algorithm improves the fused image'sobjective evaluation index sig- nificantly, obtainsa prominent infrared target and better fusion image quality.

A novel image fusion algorithm based on nonsubsampled shearlet transform

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on nonsubsampled shearlet transform (NSST) is proposed. Firstly, the NSST is utilized to decompose the source images on various scales and in different directions, and the low frequency sub-band and bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and an adaptive ‘weighted averaging’ fusion rule based on the sigmoid function and the extracted features is presented. To improve the quality of fused image, a novel sum-modified-Laplacian (NSML), which can extract more useful information from source images, is employed as the measurement to select bandpass sub-band coefficients. Finally, the fused image is obtained by performing the inverse NSST on the combined coefficients. The proposed fusion method is verified on several sets of multi-source images, and the experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.

Color video compression using color mapping into textured grayscale video frames

Color video compression has become a necessity in the advent of newer applications of color video and its increasing size for higher definitions. New approaches are used to achieve more compression over the traditional and standard compression techniques. Considerable progress has been made of color transfer-based compression techniques that do not modify standard encoders but add some pre-processing and post-processing steps. We present a novel, simple yet robust, color transfer-based compression technique that can be integrated with the standard encoders like MPEG-2, H.264/AVC, etc. We achieve the compression by discarding the color components from all frames in a video sequence except those of the Intra (I) frames. The colored intra frames are converted to gray-textured images. The method is developed based on embedding the colors of the intra frames to low visibility high frequency textures of its gray luminance image. This is achieved by decomposing the luminance of the intra frame using the non-decimated DWT or discrete wavelet frame transform and replacing the bandpass subbands by the chrominance signals. The lowpass band is same as that of the luminance signal. At the output of the decoder, the colored intra frames are recovered using the wavelet transform. Instead of calculating motion vectors, we identify and reuse the motion vectors present in the decoder. The remaining luminance frames of the video are colored with the help of identified motion vectors and the color information of the preceding frames. The proposed codec considerably improves the compression ratio achievable by the standard codec, at the cost of slight increase in computation time at the decoder end.

Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

In this paper, a new method based on nonsubsampled contourlet transform (NSCT) is proposed to fuse the infrared image and the visible light image, which will produce a new fused image by which the target can be identified more easily. Firstly, two original images were decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by using NSCT. Secondly, the selection of the low frequency subband coefficient and the bandpass direction subband coefficient is discussed in detail. The low frequency subband coefficients are selected based on the regional visual characteristics. For the selection of bandpass direction subband coefficients, this paper proposes a minimum regional cross-gradient method, and the cross-gradient is gained by calculating the gradient between the pixel of bandpass subbands and the adjacent pixel in the fused image of the low-frequency components. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.

Counteracting geometrical attacks on robust image watermarking by constructing a deformable pyramid transform

Counteracting geometrical attacks remains one of the most challenging problems in robust watermarking. In this paper, we resist rotation, scaling, and translation (RST) by constructing a kind of deformable pyramid transform (DPT) that is shift-invariant, steerable, and scalable. The DPT is extended from a closed-form polar-separable steerable pyramid transform (SPT). The radial component of the SPT's basis filters is taken as the kernel of the scalable basis filters, and the angular component is used for the steerable basis filters. The shift-invariance is inherited from the SPT by retaining undecimated high-pass and band-pass subbands. Based on the designed DPT, we theoretically derive interpolation functions for steerability and scalability and synchronization mechanisms for translation, rotation, and scaling. By exploiting the preferable characteristics of DPT, we develop a new template-based robust image watermarking scheme that is resilient to RST. Translation invariance is achieved by taking the Fourier magnitude of the cover image as the DPT's input. The resilience to rotation and scaling is obtained using the synchronization mechanisms for rotation and scaling, for which an efficient template-matching algorithm has been devised. Extensive simulations show that the proposed scheme is highly robust to geometrical attacks, such as RST, cropping, and row/column line removal, as well as common signal processing attacks such as JPEG compression, additive white Gaussian noise, and median filtering.

A Novel Image Fusion Method Based on FRFT-NSCT

Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images

Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries.

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.

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT–PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.

Fast extraction of wavelet-based features from JPEG images for joint retrieval with JPEG2000 images

In this paper, some fast feature extraction algorithms are addressed for joint retrieval of images compressed in JPEG and JPEG2000 formats. In order to avoid full decoding, three fast algorithms that convert block-based discrete cosine transform (BDCT) into wavelet transform are developed, so that wavelet-based features can be extracted from JPEG images as in JPEG2000 images. The first algorithm exploits the similarity between the BDCT and the wavelet packet transform. For the second and third algorithms, the first algorithm or an existing algorithm known as multiresolution reordering is first applied to obtain bandpass subbands at fine scales and the lowpass subband. Then for the subbands at the coarse scale, a new filter bank structure is developed to reduce the mismatch in low frequency features. Compared with the extraction based on full decoding, there is more than 72% reduction in computational complexity. Retrieval experiments also show that the three proposed algorithms can achieve higher precision and recall than the multiresolution reordering, especially around the typical range of compression ratio.

Parallel Continuous-Time $\Delta\Sigma$ ADC for OFDM UWB Receivers

A parallel multibit continuous-time (CT) DeltaSigma analog-to-digital converter for an orthogonal-frequency-division-multiplexing (OFDM) ultrawideband receiver intended to operate according to the IEEE 802.15.3a or the ECMA 368 (ISO/IEC 26907) standards has been designed. The overall CT DeltaSigma converter consists of two modulators covering two unequal subbands (low-pass (LP) and bandpass (BP) subbands) that are arranged to operate in parallel and whose respective noise transfer functions (NTFs) are designed to match its corresponding frequency band. The composite NTF for the overall converter is defined as the minimum gain value out of these two individual NTFs. The LP and BP subbands were designed by using third- and fourth-order modulators, respectively, based on a 3-bit quantizer and operating at a clock frequency of 1056 MHz. NTF zero locations were optimized according to the criterion that all the in-band composite NTF gain maxima have approximately the same value. Combining OFDM signal characteristics and converter parameters, the effect of the quantization noise on the overall converter performance has been analytically derived. A simulation program has been realized to verify the performance of the converter.

Color to gray and back: color embedding into textured gray images

We have developed a reversible method to convert color graphics and pictures to gray images. The method is based on mapping colors to low-visibility high-frequency textures that are applied onto the gray image. After receiving a monochrome textured image, the decoder can identify the textures and recover the color information. More specifically, the image is textured by carrying a subband (wavelet) transform and replacing bandpass subbands by the chrominance signals. The low-pass subband is the same as that of the luminance signal. The decoder performs a wavelet transform on the received gray image and recovers the chrominance channels. The intent is to print color images with black and white printers and to be able to recover the color information afterwards. Registration problems are discussed and examples are presented.

Linear phase cosine modulated maximally decimated filter banks with perfect reconstruction

We propose a novel way to design maximally decimated FIR cosine modulated filter banks, in which each analysis and synthesis filter has a linear phase. The system can be designed to have either the approximate reconstruction property (pseudo-QMF system) or perfect reconstruction property (PR system). In the PR case, the system is a paraunitary filter bank. As in earlier work on cosine modulated systems, all the analysis filters come from an FIR prototype filter. However, unlike in any of the previous designs, all but two of the analysis filters have a total bandwidth of 2/spl pi//M rather than /spl pi//M (where 2M is the number of channels in our notation). A simple interpretation is possible in terms of the complex (hypothetical) analytic signal corresponding to each bandpass subband. The coding gain of the new system is comparable with that of a traditional M-channel system (rather than a 2M-channel system). This is primarily because there are typically two bandpass filters with the same passband support. Correspondingly, the cost of the system (in terms of complexity of implementation) is also comparable with that of an M-channel system. We also demonstrate that very good attenuation characteristics can be obtained with the new system.