Earticle

A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds automatically during moulding production. This paper proposes a wavelet transform foreign detection method based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the effectiveness of our approach by evaluating the labeled data.

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

In this paper, data hiding algorithm using Discrete Wavelet Transform (DWT) and Arnold Transform is proposed. The secret data is scrambled using Arnold Transform to make it secure. Wavelet subbands of a cover image are obtained using DWT. The scrambled secret data is embedded into significant wavelet coefficients of subbands of a cover image. The proposed algorithm is robust to a variety of attacks like JPEG and JPEG2000 compression, image cropping and median filtering. Experimental results show that the PSNR of the composite image is 1.05 dB higher than the PSNR of existing algorithms and capacity is 25% higher than the capacity of existing algorithms.

The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.

In this paper, an accurate segmentation algorithm of video dynamic background image (VDBI) based on improved wavelet transform is proposed. Based on the smooth processing of VDBI, the traditional wavelet transform process is improved, and the two-layer decomposition of dynamic image is realized by using two-dimensional wavelet transform. On the basis of decomposition results and information enhancement processing, image features are detected, feature points are extracted, and quantum ant colony algorithm is adopted to complete accurate segmentation of the image. The maximum SNR of the output results of the proposed algorithm can reach 73.67 dB, the maximum time of the segmentation process is only 7 seconds, the segmentation accuracy shows a trend of decreasing first and then increasing, and the global maximum value can reach 97%, indicating that the proposed algorithm effectively achieves the design expectation.

본 논문은 EZW 알고리즘의 특성을 이용하여 영상의 중요 부분을 추출하는 방법과 이 방법에 의해 중요 부분이 추출 되었을 때에 중요 계수에만 워터마크를 삽입하는 방법을 제안하였다. 먼저 영상을 웨이블릿 변환 후 제로 트리 코딩을 하여 중요 계수를 검출하고, 검출된 중요 계수에 워터마크 이진 영상을 삽입하였다. 기존의 방법들은 각 대역내 계수들간의 상관 관계를 이용하여 워터마크를 삽입한 반면, 본 논문에서는 웨이블릿 변환된 영상의 계수 값이 동일한 방향을 갖는 대역 사이에서 상관관계를 갖는다는 점을 이용하여 워터마크를 삽입하였다. 실험 결과, 제안한 방법은 기존의 방식에 비하여 각종 공격에서의 강인함을 보였으며 점진적 전송이 요구되는 분야와 영상 자료를 검색하는 분야에 응용될 수 있을 것으로 기대된다.

In this paper, We proposed extraction method using EZW a specific character and then add watermark significant coefficient of image. After wavelet transform in image, the significant coefficient value add to watermark information image. In this method, the locations of nonzero wavelet coefficients are encoded with a tree structure, called zerotree, which can exploit the self-similarity of the pyramid decomposition across different scales. The simulation shows that this method provides a superior performance over conventional method and can be successfully applied to the application areas that requires of progressive transmission and search for image data.

영상융합의 목적은 다양한 영상으로부터의 정보를 하나의 영상으로 통합하는 것이다. 위성영상의 융합에 있어, 고해상도의 전정영상과 저해상도의 다중분광영상을 결합하기 위해 많은 영상 융합기법들이 제안되어왔으며, 융합결과의 공간적 세밀함과 분광정보 모두를 보존하는 것은 매우 중요하다. 웨이블릿 변환을 이용한 영상 융합기법은 분광정보의 보존 측면에서 다른 융합 기법에 비해 좋은 결과를 나타낸다. 본 연구에서는 IKONOS 영상을 이용한 수정된 a'trous 알고리즘 기반 웨이블릿 영상융합 기법을 제안한다. IKONOS 영상의 실험결과, 기존의 a'trous 알고리즘을 이용한 융합기법에 비해 공간적 세밀함과 분광정보의 보존측면에서 더 효과적인 기법임을 확인할 수 있었다.

The object of image fusion is to integrate information from multiple images as the same scene. In the satellite image fusion, many image fusion methods have been proposed for combining a high resolution panchromatic(PAN) image with low resolution multispectral(MS) images and it is very important to preserve both the spatial detail and the spectral information of fusion result. The image fusion method using wavelet transform shows good result compared with other fusion methods in preserving spectral information. This study proposes a modified a'trous algorithm based wavelet image fusion method using IKONOS image. Based on the result of experiment using IKONOS image, we confirmed that proposed method was more effective in preserving spatial detail and spectral information than existing fusion methods using a'trous algorithm.

Firstly the Decomposition Least Mean (D‐LMS) algorithm is analyzed deeply in theoretical, then a variable step size D‐LMS algorithm is proposed. By using the MATLAB based simulation model, the system identification results of the stable step size D‐LMS algorithm and the ones of the variable step‐size D‐LMS algorithm are both obtained. The simulation results show that compared with the stable step size D‐LMS algorithm, this variable step‐size D‐LMS algorithm based on wavelet transform has the better convergence ability and tracing ability, but the less state errors.

최근 CCTV 카메라와 스마트폰 카메라를 비롯한 각종 상 획득 장치들이 격히 증가하고 있으며, 이들 장치들은 매 순간마다 엄청나게 많은 상 데이터를 생성하고 있다. 이러한 방한 상 데이터를 어떻게 하면 효율으로 활용할 것인가 하는 것이 오늘날 지능형 비디오 시스템에서 가장 큰 문제로 두되고 있다. 이를 한 가장 기본인 단계로 서 비디오 스트림에서 원하는 상 임을 효과으로 찾아내는 것이 요하다. 본 논문에서는 상 특징에 기반하 는 3-단계 Haar 웨이블릿 변환을 사용하는 비디오 쿼리 알고리즘을 제안한다. 웨이블릿 변환이 가지는 계층 구조는 공간 역과 스일링 역에서의 특징을 사용가능하게 한다. 본 논문에서는 입력되는 상의 3-단계 Haar 웨이블릿 특징과 비디오 스트림에서의 각 임의 Haar 웨이블릿 특징과의 정합에 의해 원하는 임을 찾도록 한다. 실험 결과, 제안된 방법이 다른 알고리즘에 비해 정확도에서 약 1~8% 우수한 성능을 보이는 것을 확인하다.

Recently image capturing devices such as CCTV camera and smartphone camera are increasing rapidly, these devices produce a lot of image data in every moment. Using huge image data efficiently is one of the most important and big problem of modern intelligent video system. As a basic fundamental step for an intelligent video system, finding a specific image frame from a video stream is very important. This paper proposes a video query algorithm using 3-level Haar wavelet transform based on image features. Hierarchical structure of wavelet transform can have features in both space domain and scaling domain. In this paper, we can find a specific frame by matching 3-level Haar wavelet transform features of input query image and Haar wavelet features of every frame in the stream. Experimental results ascertain that the proposed algorithm has 1~8% superior performance in querying accuracy to other algorithms.

This paper proposes a Coiflet wavelet algorithm based on FPGA to realize the real-time detection of power quality transient disturbance. As to the real-time problem of wavelet algorithm software applications, the algorithm is realized on the FPGA. When Coiflet wavelet is taken as basic functions, the effect of the detecting signal singularity is good. Because of its symmetry in the time-frequency domain the algorithm could avoid the signal distortion during decomposition and reconstruction, which is convenient for subsequent signal processing. By simulating test of several kinds of signal source, the begin-end time of signal transient disturbance is contrasted. The test results show that the method is more accurate and feasible on real-time detection of power quality transient disturbance.

Electrical capacitance tomography (ECT) image reconstruction is a typical ill-posed problem. Tikhonov regularization algorithm is one of the popular methods to solve ill-posed problem. Because functional for the standard Tikhonov regularization algorithm is over-smoothness. The reconstruction image could miss edge information and the quality is not ideal. This paper proposes an image fusion algorithm based on Tikhonov regularization algorithm and wavelet transform, which can correct the edge of the reconstruction image. The simulation proves that it can enhance edges and improve the quality of the reconstruction image.

In this study, the algorithm that applies Wavelet and multi-scale analysis to remote sensing images is proposed for region variation detection on Markov random field. First of all, the Wavelet transform is adopted to decompose an original image into several sub-images, then the Mahalanobis distance decision function is used to detect the changes in different scale images, and finally the Markov random field is applied to fuse the change detection results at different scales. Since the Markov random field fusion method takes full account of the correlation between the adjacent pixels and the links of the change detection results at different scales, the fusion results are accurate and practical. The testing results prove that the studied algorithm is effective and robust.

In this paper, we propose a novel approach to explore image denoising for patch based image process. The importance measurement model of Rough Entropy and the importance reduction method of wavelet coefficients are given. This paper combines the rough set theory with the denoising method of wavelet threshold, regarding the high-frequency information blocks in the transform domain as similar ones, and adopting importance Reduction Methods to contract the coefficients. The simulation results show that this method is effective.

Aiming at the visual effect and the fusion quality is poor of the traditional image fusion algorithm, the new fusion algorithm is adopted that combining the Canny operator with taking big window contrast. The algorithm uses wavelet transform to the original images are decomposed firstly, the process will be based on the frequency characteristics of the image information to form two pieces of information, low frequency and high frequency information. Low frequency subband uses weighted average fusion algorithm, while high frequency subband uses the algorithm which combines Canny operator with taking big window contrast, the last fusion image is got from the wavelet reconstruction in contrast to the principle of wavelet decomposition . The corresponding simulation analysis was carried out on the fusion algorithm, and the visual effect is more clear. Also the object evaluation standard increased significantly.

Wavelet denoising is a commonly used method in the field of image denoising algorithms, based on the analysis of the characteristics of estimated wavelet coefficients in the wavelet threshold denoising and the traditional soft threshold and hard threshold method, in the light of the discontinuity of hard threshold and fixed deviation of soft threshold, and non-differentiable of compromise algorithm, a wavelet threshold denoising algorithm is put forward which is continuous based on the wavelet coefficients and differentiable and variable threshold value deviation, the experimental results show that this method has better denoising effect and has good practical value.

ACA (Ant Colony Algorithm) is a smart global random search algorithm, which is combined with blind equalization algorithm. In optimizing and initializing equalizer weight vector, ACA can avoid falling into local extremum easily in Stochastic Gradient Descent Algorithm of CMA and improve the convergence speed and reduce the steady-state mean square error greatly. This paper mainly studies the ACA to initialize the equalizer weight vector and the theory of wavelet transform, comes up with the wavelet blind equalization algorithm based on Ant Colony Optimization (ACA-WTCMA).The simulation results of underwater acoustic channel verify the effectiveness of the algorithm.

This paper presents a new recognition algorithm for plant pathology images based on the Non-negative Matrix Factorization, the proposed algorithm is combined with optimal wavelet packet basis to recognize patterns and conduct data encoding in the internet of things oriented intelligent agricultural system. The experimental results show that the performance of the proposed recognition algorithm is far better than those of the principal component analysis and linear discriminant analysis, and the recognition rate are improved, on average, about 14.65% and 11.18% higher than the rates of the above algorithms respectively. The presented algorithm is characterized by the fast speed, high calculation accuracy and easy hardware implementation.