Traditional Discrete Wavelet Transform Research Articles

Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter

ABSTRACT Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality.

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

This paper introduces an optimal solution for wavelet-based medical image fusion using different wavelet families and Principal Component Ana1ysis (PCA) based on the Modified Central Force Optimization (MCFO) technique. The main motivation of this work is to increase the quality of medical fused images in order to provide correct diagnosis of diseases for the objective of optimal therapy. This can be achieved by fusing medical images of different modalities using an optimization technique based on the MCFO. The MCFO technique gives the optimum gain parameters that achieve the best fused image quality. Histogram matching is applied to improve the overall values of the Peak Signal-to-Noise Ratio (PSNR), entropy, local contrast, and quality of the fused image. A comparative study is performed between the proposed algorithm, the traditional Discrete Wavelet Transform (DWT), and the PCA fusion using maximum fusion rule. The proposed algorithm is evaluated subjectively and objectively with different fusion quality metrics. Simulation results demonstrate that the proposed MCFO optimized wavelet-based fusion algorithm using Haar wavelet and histogram matching achieves a superior performance with the highest image quality and clearest image details in a very short processing time.

Finger Vein Recognition using Rotated Wavelet Filters

Finger vein biometric have been recognized as the most effective and promising recognition method due to its accuracy and security. This paper discusses a method for finger vein image features extraction using 2-D Rotated Wavelet Filters (RWF) and Discrete Wavelet Transform (DWT) jointly. A set of 2-D RWF filters improves characterization of diagonally oriented texture features from a finger vein image. The 2-D RWF and DWT jointly used for decomposition of a finger vein image ROI up to third level. The standard deviation and energy of each subband from every decomposition level are used for the creation of features vector. Then Canberra distance classifier is used for the classification of finger vein images. The performance of this method has evaluated on the standard finger vein image database of Shandong University (SDUMLA), China. Experimental results have shown that the method with RWF and DWT jointly gives better results as compare to the traditional DWT based methods. General Terms Pattern recognition and classification, Biometrics, Wavelet Transform.

ZM-SPECK: A Fast and Memoryless Image Coder for Multimedia Sensor Networks

The set partitioned embedded block (SPECK) algorithm is an efficient block-based image coder to encode wavelet transformed images. SPECK uses linked lists to track significant/insignificant coefficients and block sets, thereby having a large memory requirement that increases with the encoding rate. Furthermore, multiple memory read/write operations and list management slow down the algorithm. In addition, the implementation of the traditional discrete wavelet transform (DWT) is memory intensive and time-consuming. Therefore, it is difficult to implement image coding using the traditional DWT and SPECK algorithm on low-cost visual sensor nodes. Most of the existing studies on low-memory implementations of the SPECK algorithm attempt to replace the dynamic memory of linked lists by a static memory in the form of fixed-length state tables/markers. In this paper, a fast and memoryless image coder is proposed, which uses the fractional wavelet filter to calculate the DWT coefficients of the image and a zero-memory listless SPECK algorithm for quantization and coding of the DWT coefficients. The proposed algorithm, referred as zero-memory SPECK (ZM-SPECK), completely eliminates the linked lists and only uses a few registers to perform some low-level arithmetic/logical operations. The elimination of linked lists also reduces the memory access time, thereby making ZM-SPECK faster than the original SPECK algorithm. Simulation results show that the proposed ZM-SPECK coder outperforms the contemporary state-of-the-art wavelet image coders in terms of memory requirement and computational complexity, while retaining their coding efficiency. The proposed ZM-SPECK image coder is thus very well suited for image communication in visual sensor networks.

A fast Discrete Wavelet Transform algorithm for visual processing applications

For visual processing applications, the two-dimensional (2-D) Discrete Wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific application, the four-band decomposition demands a huge complexity and transpose time. This work presents a fast algorithm, namely 2-D Symmetric Mask-based Discrete Wavelet Transform (SMDWT), to address some critical issues of the 2-D DWT. Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands as mentioned, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop lower complexity schemes for DWT. The proposed SMDWT has been proved as a highly efficient and independent processing to yield target subbands, which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any possible DWT-based applications.

The secure wavelet transform

There has been an increasing concern for the security of multimedia transactions over real-time embedded systems. Partial and selective encryption schemes have been proposed in the research literature, but these schemes significantly increase the computation cost leading to tradeoffs in system latency, throughput, hardware requirements and power usage. In this paper, we propose a light-weight multimedia encryption strategy based on a modified discrete wavelet transform (DWT) which we refer to as the secure wavelet transform (SWT). The SWT provides joint multimedia encryption and compression by two modifications over the traditional DWT implementations: (a) parameterized construction of the DWT and (b) subband re-orientation for the wavelet decomposition. The SWT has rational coefficients which allow us to build a high throughput hardware implementation on fixed point arithmetic. We obtain a zero-overhead implementation on custom hardware. Furthermore, a Look-up table based reconfigurable implementation allows us to allocate the encryption key to the hardware at run-time. Direct implementation on Xilinx Virtex FPGA gave a clock frequency of 60 MHz while a reconfigurable multiplier based design gave a improved clock frequency of 114 MHz. The pipelined implementation of the SWT achieved a clock frequency of 240 MHz on a Xilinx Virtex-4 FPGA and met the timing constraint of 500 MHz on a standard cell realization using 45 nm CMOS technology.

Shift Invariant Wavelet Denoising of Ultrasonic Traces

Basic wavelet denoising techniques rely on a thresholding of the discrete wavelet transform (DWT) coefficients of the noisy signal. Some improvements in noise reduction efficiency can be obtained by the use of shift-invariant undecimated wavelet transforms (UWT). Ultrasonic grain noise is one of the most usual types of noise present in ultrasonic non-destructive evaluation. It comes from reflections in the material structure, and occupies a frequency band very similar to that of the echosignals of interest. In this work, new advances in the application of redundant wavelet transforms to ultrasonic grain noise reduction are presented. Wavelet denoising is applied to several sets of synthetic ultrasonic traces, which are obtained from a model that includes frequency dependent attenuation for both grain and flaw backscattered echoes, frequency dependent scattering from the grains, and an accurate model for the pulse-echo frequency response of the piezoelectric ultrasonic transducer. Two processors based on traditional DWT and alternative a trous UWT denosing have been implemented and compared, using level dependent thresholds (appropriate for correlated noise), soft thresholding, and Universal, SURE and Minimax threshold selection rules. The performances of the two processors are analyzed in terms of the mean value and standard deviation of the signal-to-noise ratio (SNR) of different sets of ultrasonic traces before and after denoising. It is shown that a trous UWT processing provides better results than DWT with a general tendency to higher quality of the resulting traces and greater robustness of the processing. It is also shown that the better performance of the UWT is mainly related to the redundancy of the representation, since there are not significant variations between the threshold values obtained with each processor.

Method of wavelet domain denoising based on directional rectangle window

According to the features of the wavelet transform,a new method of choosing the local neighborhood was proposed.For the three subbands of the same scale,we chose rectangle windows of different directions,and the size of the rectangle windows for different scales is also different.For the multidirectional images,Dual-Tree Complex Wavelet Transform(DTCWT)was employed instead of the traditional Discrete Wavelet Transform(DWT).The experimental results indicate that the method of choosing directional rectangle windows is simple and effective,and high PSNR and good visual quality can be obtained from this method.

Texture image retrieval using rotated wavelet filters

A novel approach for texture image retrieval is proposed by using a new set of two-dimensional (2-D) rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly. A new set of 2-D rotated wavelet improves characterization of diagonally oriented textures. Experimental results indicate that the proposed method improves retrieval rate from 70.09% to 78.44% on database D1, and from 75.62% to 80.78% on database D2, compared with the traditional DWT based approach. The proposed method also retains comparable levels of computational complexity.