Wavelet Image Enhancement Research Articles

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance.
 Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques.
 In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.

Ultrasonic Infrared Thermography of Aluminium Thin Plates for Crack Inspection in Friction Stir Welded Joints

Ultrasonic infrared thermography, thanks to its defect selective, dark-field damage detection capability, is a powerful tool for the inspection of cracks, but the ultrasound coupling is difficult for thin-walled or low-rigidity specimens if an external stimulation is employed, and the crack signature is poor for metals with high thermal conductivities. In order to detect cracks in friction stir welded joints of aluminium alloy thin plates, the constraint-excitation schemes and post image processing methods were investigated. A base excitation combined with unsymmetrical constraint condition of the thin plate were proposed and analyzed by using experiment and vibration modal analysis; a wavelet image enhancement based on symlets or biorthogonal wavelets was presented. The base excitation skill with unsymmetrical support condition and the post image processing with wavelet image enhancement were high-lighted. The results showed that the new constraint-excitation scheme was a feasible approach to detect kissing cracks in friction stir welded joints of thin plates; the wavelet image enhancement method was effective. The presented measures can greatly improve the detectability of ultrasonic thermography for thin plates.

A quality quantitative method of silicon direct bonding based on wavelet image analysis

The rapid development of MEMS (micro-electro-mechanical systems) has received significant attention from researchers in various fields and subjects. In particular, the MEMS fabrication process is elaborate and, as such, has been the focus of extensive research inquiries. However, in MEMS fabrication, component bonding is difficult to achieve and requires a complex approach. Thus, improvements in bonding quality are relatively important objectives. A higher quality bond can only be achieved with improved measurement and testing capabilities. In particular, the traditional testing methods mainly include infrared testing, tensile testing, and strength testing, despite the fact that using these methods to measure bond quality often results in low efficiency or destructive analysis. Therefore, this paper focuses on the development of a precise, nondestructive visual testing method based on wavelet image analysis that is shown to be highly effective in practice. The process of wavelet image analysis includes wavelet image denoising, wavelet image enhancement, and contrast enhancement, and as an end result, can display an image with low background noise. In addition, because the wavelet analysis software was developed with MATLAB, it can reveal the bonding boundaries and bonding rates to precisely indicate the bond quality at all locations on the wafer. This work also presents a set of orthogonal experiments that consist of three prebonding factors, the prebonding temperature, the positive pressure value and the prebonding time, which are used to analyze the prebonding quality. This method was used to quantify the quality of silicon-to-silicon wafer bonding, yielding standard treatment quantities that could be practical for large-scale use.

An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

Electrical capacitance tomography (ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance. To solve the nonlinear and ill-posed inverse problem: image reconstruction of ECT system, this paper proposed a new image reconstruction method based on improved radial basis function (RBF) neural network combined with adaptive wavelet image enhancement. Firstly, an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured. Then, for better image quality, adaptive wavelet image enhancement technique was emphatically analyzed and studied, which belongs to a space-frequency analysis method and is suitable for image feature-enhanced. Through multi-level wavelet decomposition, edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band; noise distribution in the space-frequency domain can be estimated based on statistical characteristics; after that a self-adaptive edge enhancement gain can be constructed. Finally, the image is reconstructed with adjusting wavelet coefficients. In this paper, a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm. Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement, and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal (PC)/air].