3D Wavelet Transform Research Articles

Image analysis of soft-tissue in-growth and attachment into highly porous alumina ceramic foam metals

The detailed quantitative characterization of soft-tissue in-growth into highly porous artificial implants is critical to understanding the biophysical processes that will lead to the best structural scaffolding construct. Previous studies have performed mechanical peel tests and mostly qualitative histological analyses of soft-tissue. The goal of this paper is to report the results obtained from applying two image analysis algorithms to quantify the morphological structure found in histological images of stained soft-tissue in-growth into alumina ceramic foam metal implants using a canine model. Three different pore sizes were used and three different post-operative time points were considered. Using the 2D Wavelet Transform Modulus Maxima method and 2D Fourier Transform analysis, a strong anisotropic signature (directional preference) is detected in early (4-week) histological samples. The direction of preference is towards the center of the implants. The strength of the anisotropy at later time points (8 and 16 weeks) becomes gradually weaker. Our interpretation is that after a short period of time, the main tissue growth activity has been concentrated on filling the artificial implant by growing towards its center. The weaker anisotropic signature found at later time points is interpreted as the tissue growth activity strengthening its structure by growing in more random directions.

An edge detection approach based on directional wavelet transform

The standard 2D wavelet transform (WT) has been an effective tool in image processing. In recent years, many new transforms have been proposed successively, such as curvelets, bandlets, directional wavelet transform etc, which inherit the merits of the standard WT, and are more adequate at the 2D image processing tasks. Intuitively, it seemed that applying these novel tools to edge detection should acquire finer performance. In this paper, we propose an edge detection approach based on directional wavelet transform which retains the separable filtering and the simplicity of computations and filter design from the standard 2D WT. In addition, the corresponding gradient magnitude is redefined and a new algorithm for non-maximum suppression is described. The experimental results of edge detection for several test images are provided to demonstrate our approach.

Robustness of Watermarking Spectral Images with 3D Wavelet Transform Subject to Various Illumination Conditions

Robustness of Watermarking Spectral Images with 3D Wavelet Transform Subject to Various Illumination Conditions

Development and Evaluation of High-Performance Decorrelation Algorithms for the Nonalternating 3D Wavelet Transform

We introduce and evaluate the implementations of three parallel video-sequences decorrelation algorithms. The proposed algorithms are based on the nonalternating classic three-dimensional wavelet transform (3D-WT). The parallel implementations of the algorithms are developed and tested on a shared memory system, an SGI origin 3800 supercomputer making use of a message-passing paradigm. We evaluate and analyze the performance of the implementations in terms of the response time and speed-up factor by varying the number of processors and various video coding parameters. The key points enabling the development of highly efficient implementations rely on the partitioning of the video sequences into groups of frames and a workload distribution strategy supplemented by the use of parallel I/O primitives, for better exploiting the inherent features of the application and computing platform. We also evaluate the effectiveness of our algorithms in terms of the first-order entropy.

An efficient implementation of a 3D wavelet transform based encoder on hyper-threading technology

Video medical compression algorithms based on the 3D wavelet transform obtain both excellent compression rates and very good quality, at the expense of a higher execution time. The goal of this work is to improve the execution time of our 3D Wavelet Transform Encoder. We examine and exploit the characteristics and advantages of a hyper-threading processor. The Intel Hyper-threading Technology (HT) is a technique based on simultaneous multi-threading (SMT), which allows several independent threads to issue instructions to multiple functional units in a single cycle. In particular, we present two approaches: data-domain and functional, which differ in the way that the decomposition of the application is performed. The first approach is based on data division, where the same task is performed simultaneously by each thread on an independent part of the data. In the second approach, the processing is divided in different tasks that are executed concurrently on the same data set. Based on the latter approach, we present three proposals that differ in the way that the tasks of the application are divided between the threads. Results show speedups of up to 7% and 34% by the data-domain and functional decomposition, respectively, over a version executed without hyper-threading technology. Finally, we design several implementations of the best method with Pthreads and OpenMP using functional decomposition. We compare them in terms of execution speed, ease of implementation and maintainability of the resulting code.

Nanoscale displacement measurement by a digital nano-moiré method with wavelettransformation

A digital nano-moiré method with wavelet transformation is explored to measure nanoscalein-plane displacement fields. By applying e-beam lithography, a periodic PMMAnanostructure array is fabricated directly on the specimen and used as the specimengrating. Moiré patterns are generated by overlapping the images of the PMMA specimengrating obtained from AFM scanning and the virtual reference grating produced by adigital image generating process. Then, the overlapped images are filtered by the 2Dwavelet transformation (WT) to capture the target moiré patterns. Existing methods, byoverlapping the monitor-generated scanning lines with the image of the specimengrating, cause a mismatch problem. Previously, the carrier moiré method wasexplored with the aim of curing the mismatch problem. Unfortunately, the carriermoiré method, in addition to suffering from increased complexity of mathematicalcalculations, is incapable of directly obtaining the displacement field. Thus, themismatch problem will result in inconveniences and restrictions in the practicalapplication. Instead of using monitor-generated scanning lines, the proposed methodapplies the virtual reference grating, and thus puts the mismatch problem to rest.Nevertheless, the resultant moiré image suffers from low contrast which, if leftuntreated, might distort the measurement result. Therefore, the WT, known for itssharpened abilities of characteristic and edge detection, is used to capture the targetmoiré patterns and improve the measurement accuracy. The proposed method hasbeen carried out in the laboratory. Experimental results have shown that theproposed method is convenient and efficient for nanoscale displacement measurement.

Microprocessor-based FPGA implementation of SPIHT image compression subsystems

This paper presents a microprocessor-based FPGA system for lossy image compression. The system implements a widely known wavelet-based compression method, i.e. the Set Partitioning In Hierarchical Trees algorithm (SPIHT). The computationally intensive 2D wavelet-transform is performed by means of custom circuits, whereas an embedded microprocessor is used to execute a purpose-build SPIHT encoding process. The aim of this work is to demonstrate and verify the feasibility of a compact and programmable image compression sub-system that uses just one low-cost FPGA device. The entire system consumes just 1637 slices of an XC2V chip, it runs at 100 MHz clock frequency and reaches a speed performance suitable for several real-time applications.

Breast volume denoising and noise characterization by 3D wavelet transform

Breast imaging through cone-beam computed tomography provides a digital breast volume, with which the three-dimensional (3D) breast tissues can be analyzed. Data denoising, as a preprocessing step for subsequent volumetric breast segmentation is always needed. In this paper, we report a volumetric denoising technique by a separable 3D wavelet transform (WT), i.e. a ‘2D WT plus 1D WT’ scheme. Specifically, the scheme performs two-dimensional (2D) wavelet denoising on a stack of slice images of the breast volume, followed by one-dimensional (1D) wavelet denoising along the stacking direction. The denoising is achieved by wavelet decomposition, high-pass subband attenuation, and wavelet synthesis. A one-level 3D WT produces eight subbands occupying the octants of the 3D wavelet space. Multilevel WT also provides a multiresolution representation of breast volume, i.e. a sequence of low-pass subbands. In general, most noise and irregularity features are imparted into the high-pass subbands, which are removed or reduced for denoising purpose. Meanwhile, the information in a subband can be characterized in terms of energy, variance, and entropy. Through 3D visualization, the spatial structure in a subband can also be visually perceived. Experimental demonstration with the breast volume reconstructed from a specimen is provided.

3D Scan-Based Wavelet Transform and Quality Control for Video Coding

Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during video playback. In this paper, we propose a new temporal scan-based wavelet transform method for video coding combining the advantages of wavelet coding (performance, scalability) with acceptable reduced memory requirements, no additional CPU complexity, and avoiding jerks. We also propose an efficient quality allocation procedure to ensure a constant quality over time.