A Note on Tight Frames

Suppose that Φ(x)∈L 2(R) with compact support and V= span{Φ(x-k)|k∈Z}. In this note, we prove that if {Φ(x-k)k|k∈Z} is tight frame with bound 1 in V, then {Φ(x-k)|k∈Z} must be an orthonormal basis of V.

Expansion of Frames to Tight Frames

We show that every Bessel sequence （and therefore every frame） in a separable Hilbert space can be expanded to a tight frame by adding some elements. The proof is based on a recent generalization of the frame concept, the g-frame, which illustrates that g-frames could be useful in the study of frame theory. As an application, we prove that any Gabor frame can be expanded to a tight frame by adding one window function.

Construction for a class of smooth wavelet tight frames

From the inequality |P(z)|2 + |P(-z)|2 ≤1, assuming that both of the low-pass filters and high-pass filters are unknown, we design compactly supported wavelet tight frames. The unknowing of low-pass filters allows the design more freedom, and both the low-pass filters and high-pass filters have symmetries or anti-symmetries. We give the algorithm for filters with odd and even lengths separately, some concrete examples of wavelet tight frames with the length 4, 5, 6, 7, and at last we give the result of decomposing Lena image with them.

ON NECESSARY AND SUFFICIENT CONDITIONS FOR DUALS OF WAVELET FRAMES

This paper is concerned with the characterization of the duals of wavelet frames of L(2)(R). The sufficient and necessary conditions for them are obtained.

Sparse Representation by Frames with Signal Analysis

The use of frames is analyzed in Compressed Sensing (CS) through proofs and experiments. First, a new generalized Dictionary-Restricted Isometry Property (D-RIP) sparsity bound constant for CS is established. Second, experiments with a tight frame to analyze sparsity and reconstruction quality using several signal and image types are shown. The constant is used in fulfilling the definition of D-RIP. It is proved that k-sparse signals can be reconstructed if by using a concise and transparent argument1. The approach could be extended to obtain other D-RIP bounds (i.e. ). Experiments contrast results of a Gabor tight frame with Total Variation minimization. In cases of practical interest, the use of a Gabor dictionary performs well when achieving a highly sparse representation and poorly when this sparsity is not achieved.

Construction of Multivariate Tight Framelet Packets Associated with Dilation Matrix

In this paper, we present a method for constructing multivariate tight framelet packets associated with an arbitrary dilation matrix using unitary extension principles. We also prove how to construct various tight frames for L2 （JRa） by replacing some mother framelets.

A Robust Digital Watermarking Algorithm Based on Finite-Set Discrete Radon Transform Tight Frame

Digital watermarking technology plays a powerful role in the effective protection of digital media copyright, image authentication, image sharing, image information transmission and other fields. Driven by strong demand, digital image watermarking technology has aroused widespread research interest and has gradually developed into one of the most active research directions in information science. In this paper, we present a novel robust digital watermarking algorithm based on discrete radon transform tight frame in finite-set (FDRT). FDRT of the zero mean image is a tight frame, the frame boundary A = B = 1, the dual of the frame is itself. The decomposition and reconstruction of the FDRT tight frame will not cause the phenomenon of image distortion. The embedding of hidden watermark is to add a weak signal to the strong background of the original image. Watermark extraction is to effectively identify the embedded weak signal. The feasibility of the watermarking algorithm is analyzed from two aspects of information hiding and robustness. We select the independent Gaussian random vector as the watermark series, and the peak signal-to-noise ratio (PSNR) as the visual degradation criterion of the watermark image. Basing the FDRT compact stand dual operator, we derived the relationship among the strength parameter, square sum of watermark series, the PSNR. Using Checkmark system, the simulation results show that the algorithm is robust enough to some very important image processing attacks such as lossy compression, MAP, filtering, segmentation, edge enhancement, jitter, quadratic modulation and general geometric attack (scaling, rotation, shearing), etc.

Migration scheme for imaging offset VSP data within local phase space

The imaging of offset VSP data in local phase space can improve the image of the subsurface structure near the well.In this paper,we present a migration scheme for imaging VSP data in a local phase space,which uses the Gabor-Daubechies tight framebased extrapolator（G-D extrapolator） and its high-frequency asymptotic expansion to extrapolate wavefields and also delineates an improved correlation imaging condition in the local angle domain.The results for migrating synthetic and real VSP data demonstrate that the application of the high-frequency G-D extrapolator asymptotic expansion can effectively decrease computational complexity.The local angle domain correlation imaging condition can be used to weaken migration artifacts without increasing computation.

STABLE RECOVERY OF SIGNALS WITH THE HIGH ORDER D-RIP CONDITION

This paper establishes a high order condition on the restricted isometry property adapted to a frame D （D-RIF） for the signal recovery. It is shown that if the measurementmatrix A satisfies the D-RIP condition δtk 〈t-1/t for t 〉 1, then all signals f which aresparse in terms of a tight frame D can be recovered stably or exactly via the l1-analysis model based on y= Af ＋ z in 12 and Dantzig selector bounded noise setting.

NONLINEAR APPROXIMATION WITH GENERAL WAVE PACKETS

We study nonlinear approximation in the Triebel-Lizorkin spaces with dictionaries formed by dilating and translating one single function g. A general Jackson inequality is derived for best m-term approximation with such dictionaries. In some special cases where g has a special structure, a complete characterization of the approximation spaces is derived.

Conjugate Symmetric Complex Tight Wavelet Frames with Two Generators

Two algorithms for constructing a class of compactly supported conjugate symmetric complex tight wavelet framesψ={ψ1,ψ2}are derived.Firstly,a necessary and sufficient condition for constructing the conjugate symmetric complex tight wavelet frames is established.Secondly,based on a given conjugate symmetric low pass filter,a description of a family of complex wavelet frame solutions is provided when the low pass filter is of even length.When one wavelet is conjugate symmetric and the other is conjugate antisymmetric,the two wavelet filters can be obtained by matching the roots of associated polynomials.Finally,two examples are given to illustrate how to use our method to construct conjugate symmetric complex tight wavelet frames which have some vanishing moments.

Lifting scheme of symmetric tight wavelets frames

This paper proposes a method to realize the lifting scheme of tight frame wavelet filters. As for 4-channel tight frame wavelet filter, the tight frame transforms＇ matrix is 2×4, but the lifting scheme transforms＇ matrix must be 4×4. And in the case of 3-channel tight frame wavelet filter, the transforms＇ matrix is 2×3, but the lifting scheme transforms＇ matrix must be 3×3. In order to solve this problem, we introduce two concepts： transferred polyphase matrix for 4-channel filters and transferred unitary matrix for 3-channel filters. The transferred polyphase matrix is symmetric/antisymmetric. Thus, we use this advantage to realize the lifting scheme.

Construction tight wavelet of two-direction frames

We investigate the construction of two-direction tight wavelet frames First, a sufficient condition for a two-direction refinable function generating two-direction tight wavelet frames is derived. Second, a simple constructive method of two-direction tight wavelet frames is given. Third, based on the obtained two-direction tight wavelet frames, one can construct a symmetric multiwavelet frame easily. Finally, some examples are given to illustrate the results.

Data-Driven Tight Frame for Multi-channel Images and Its Application to Joint Color-Depth Image Reconstruction

In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate piecewise smooth images.Thus,it has been widely used in many practical image restoration problems.However,images from different scenarios are so diverse that no static wavelet tight frame system can sparsely approximate all of themwell.To overcome this,recently,Cai et.al.(Appl Comput Harmon Anal 37:89–105,2014)proposed a method that derives a data-driven tight frame adapted to the specific input image,leading to a better sparse approximation.The data-driven tight frame has been applied successfully to image denoising and CT image reconstruction.In this paper,we extend this data-driven tight frame construction method to multi-channel images.We construct a discrete tight frame system for each channel and assume their sparse coefficients have a joint sparsity.The multi-channel data-driven tight frame construction scheme is applied to joint color and depth image reconstruction.Experimental results show that the proposed approach has a better performance than state-of-the-art joint color and depth image reconstruction approaches.

DATA-DRIVEN TIGHT FRAME CONSTRUCTION FOR IMPULSIVE NOISE REMOVAL

The method of data-driven tight frame has been shown very useful in image restoration problems.We consider in this paper extending this important technique,by incorporating L_(1) data fidelity into the original data-driven model,for removing impulsive noise which is a very common and basic type of noise in image data.The model contains three variables and can be solved through an efficient iterative alternating minimization algorithm in patch implementation,where the tight frame is dynamically updated.It constructs a tight frame system from the input corrupted image adaptively,and then removes impulsive noise by the derived system.We also show that the sequence generated by our algorithm converges globally to a stationary point of the optimization model.Numerical experiments and comparisons demonstrate that our approach performs well for various kinds of images.This benefits from its data-driven nature and the learned tight frames from input images capture richer image structures adaptively.

Optimal D-RIP Bounds in Compressed Sensing

This paper establishes new bounds on the restricted isometry constants with coherent tight frames in compressed sensing. It is shown that if the sensing matrix A satisfies the D-RIP condition 5k 〈 1/3 or 52k 〈 x/2/2, then all signals f with D＊f are k-sparse can be recovered exactly via the constrained l1 minimization based on y = A f, where D＊ is the conjugate transpose of a tight frame D. These bounds are sharp when D is an identity matrix, see Cai and Zhang＇s work. These bounds are greatly improved comparing to the condition 8k 〈 0.307 or 52k 〈 0.4931. Besides, if 3k 〈 1/3 or δ2k 〈 √2/2, the signals can also be stably reconstructed in the noisy cases.

Wavelets and framelets from dual pseudo splines

Dual pseudo splines constitute a new class of refinable functions with B-splines as special examples.In this paper,we shall construct Riesz wavelet associated with dual pseudo splines.Furthermore,we use dual pseudo splines to construct tight frame systems with desired approximation order by applying the unitary extension principle.

Product(α1,α2)-modulation spaces Dedicated to the Memory of Michel Marias

We study fundamental properties of product(α,α)-modulation spaces built by(α,α)-coverings of R× R.Precisely we prove embedding theorems between these spaces with different parameters and other classical spaces.Furthermore,we specify their duals.The characterization of product modulation spaces via the short time Fourier transform is also obtained.Families of tight frames are constructed and discrete representations in terms of corresponding sequence spaces are derived.Fourier multipliers are studied and as applications we extract lifting properties and the identification of our spaces with(fractional) Sobolev spaces with mixed smoothness.

A phase model for point spread function estimation in ground-based astronomy

In ground-based astronomy, images of objects in outer space are acquired via ground-based tele- scopes. However, the imaging system is generally interfered by atmospheric turbulence and hence images so acquired are blurred with unknown point spread function （PSF）. To restore the observed images, aberration of the wavefront at the telescope＇s aperture, i.e., the phase, is utilized to derive the PSF. However, the phase is not readily available. Instead, its gradients can be collected by wavefront sensors. Thus the usual approach is to use regularization methods to reconstruct high-resolution phase gradients and then use them to recover the phase in high accuracy. Here, we develop a model that reconstructs the phase directly. The proposed model uses the tight frame regularization and it can be solved efficiently by the Douglas-Rachford alternating direction method of multipliers whose convergence has been well established. Numerical results illustrate that our new model is efficient and gives more accurate estimation for the PSF.

FRAMELET BASED DECONVOLUTION

In this paper, two framelet based deconvolution algorithms are proposed. The basic idea of framelet based approach is to convert the deconvolution problem to the problem of inpainting in a frame domain by constructing a framelet system with one of the masks being the given （discrete） convolution kernel via the unitary extension principle of [26], as introduced in [6-9] . The first algorithm unifies our previous works in high resolution image reconstruction and infra-red chopped and nodded image restoration, and the second one is a combination of our previous frame-based deconvolution algorithm and the iterative thresholding algorithm given by [14, 16]. The strong convergence of the algorithms in infinite dimensional settings is given by employing proximal forward-backward splitting （PFBS） method. Consequently, it unifies iterative algorithms of infinite and finite dimensional setting and simplifies the proof of the convergence of the aluorithms of [6].