A new wavelet-based fuzzy single and multi-channel image denoising

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. We use this fuzzy feature for enhancing wavelet coefficients' information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. In addition, we extend our noise reduction algorithm for multi-channel images. We use inter-relation between different channels as a fuzzy feature for improving the denoising performance compared to denoising each channel, separately. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.     

A focus checking technique for image analysis systems

The paper describes a method of checking the accuracy of focus of images scanned with a raster scanner, by means of direct analysis of the data from a single scan. The parameter used is a measure of the high frequency response of the system to sharp edges in the image, and is obtained by comparing entries of tables of density differences between adjacent and near-adjacent pixels along scanlines. The technique has been incorporated into an autofocussing module of an automated cytology prescreening system, and has been found to give adequate warning of focus errors when scanning a wide range of normal and abnormal cytology specimens.     

Wavelet correlogram: A new approach for image indexing and retrieval

In this paper, a new algorithm for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution image decomposition and color correlation histogram. According to the new algorithm, wavelet coefficients of the image are computed first using a directional wavelet transform such as Gabor wavelets. A quantization step is then applied before computing one-directional autocorrelograms of the wavelet coefficients. Finally, index vectors are constructed using these one-directional wavelet correlograms. The retrieval results obtained by application of our new method on a 1000 image database demonstrated a significant improvement in effectiveness and efficiency compared to the indexing and retrieval methods based on image color correlogram or wavelet transform.     

Multi-focus image fusion using PCNN

This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further.     

A fast focus measure for video display inspection

In this paper, we propose a robust and computationally fast focus measure for cathode ray tubes (CRTs) found in TVs and computer and video monitors. The focus measure is represented by the area ratio of a predetermined object pattern in the test image of a fixed size, which increases in size as the degree of defocus increases. The area ratio is calculated using a simple, straightforward moment-preserving method. On-line, real-time application of focus adjustment can be realized with the proposed method. Received: 4 November 2001 / Accepted: 6 December 2002 Published online: 23 April 2003 Correspondence to. D.-M. Tsai (e-mail: iedmtsai@saturn.yzu.edu.tw)     

A real-time terrain visualization algorithm using wavelet-based compression

We propose a real-time terrain visualization algorithm combined with wavelet-based compression. Our approach updates a surface mesh model in real time by using wavelet coefficients and height data decoded from a compressed bitstream. To achieve this, a new mesh approximation method using restricted quadtree triangulation is designed on the basis of wavelet coefficients representing surface complexity. Also, a wavelet-based compression having a multiresolution structure is introduced to handle large terrain data interactively. Simulation results demonstrate that the proposed algorithm is prospective for applications in a network environment where narrow bandwidth and low computational power are usually allowed .     

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.     

Design and performance of a pixel-level pipelined-parallel architecture for high speed wavelet-based image compression

Wavelets have widely been used in many signal and image processing applications. In this paper, a new serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet transform, which is realised using some FIFO memory modules implementing a pixel-level pipeline architecture to compress and decompress images. The real filter calculation over 4 × 4 window blocks is done using a tree of carry save adders to ensure the high speed processing required for many applications. The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis reveals that the proposed architecture, implemented using current VLSI technologies, can process a video stream in real time.     

基于小波域分析的图像模糊度算法研究

针对模糊图像处理问题,提出了一种基于小波域分析的测量图像模糊程度的算法。这种算法可在图像处理中获得该图像的模糊程度(图像模糊度)的客观、准确的度量。实验结果表明：该模糊度与人的主观排序丰目吻合,可用于图像品质的评价。     

基于小波变换多聚焦图像融合算法改进

传统于小波变换多聚焦图像融合算法忽略了低频分量融合处理,这样会导致边缘失真问题.为了解决这个问题,文章提出了一种改进小波变换多聚焦图像融合算法,该算法的核心思想是对待融合图像进行多尺度分解,这样得到的图像就包含了低频图像,在对高频图像进行处理的同时,对低频图像使用局部区域梯度信息的方法进行处理.测试结果表明,和传统的融合方法相比本方法的融合结果效果更好.     

Reliability measure for shape-from-focus

Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyzes the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented.     

基于小波多尺度的图像增强新算法

传统图像增强算法在增强对比度的同时,也很大地提升图像噪声,需要对图像进行降噪处理。小波增强方法兼顾图像信号的空域和频域特性,但没有充分考虑到视觉的非线性特性。针对现有图像增强技术的这一缺陷,在分析小波变换对噪声影响规律的基础上,结合小波多尺度的特性,提出了一种基于小波多尺度的图像增强新算法,利用不同尺度上的小波系数间的相关性和小波分析的时频局部化特性来有效区分噪声和图像信息,有效改善了图像增强过程中的噪声放大问题。     

A multiscale regularity measure as a geometric criterion for image segmentation

Recent results from human vision experiments show that lines of low fractal dimension are highly capable of evoking indification with nameable objects. In other words, regular lines are recognized in human vision as object edges. In this paper, a regularity measure of discrete line geometry is presented. This quantitative measure based on a ratio between lines of varying lengths is analyzed in the framework of brownian motion theory. The measure on a given scale is always computed from the maximum precision image, so that no subresolution assumption is introduced. A choice of scale determines the quantity of global information versus local information one wants to measure. We show how this quantitative measure leads to relevant shape information. To illustrate this, an example of an image segmentation application is realized. The segmentation based essentially on geometry criteria, uses a region-growing process. The process depends on a single parameter that can be fixed in a natural way, comparing contour regularity to a geometric model regularity. We present experimental results performed on real-scene images, including indoor and outdoor images.     

基于小波变换的红外显微图像融合

图像融合是指把来自多传感器数据的互补信息合并形成一幅新的图像,以便达到进行目标监视与识别等目的。本文提出了基于小波变换的图像融合方法,并分析了可见光与红外的显微图像的融合结果。结果融合效果不错,结果图像比较清晰,在一图像中即可得到比较完整的信息。因而,这一方法具有广阔的应用前景。     

基于小波的小尺寸物体的图像边缘提取方法

基于小波变换的“多分辨”特性，采用一种利用小波模极大值，找到图像中的边界点，从而实现对灰度图像的边界检测的方法，通过分析图像中是否有灰度突变特征，来判断边界的存在。在进行边界检测之前。首先要滤除原图像的寄生噪声的干扰。实验结果表明：基于小波变换的边界检测方法可对图像边界进行敏锐提取，从而得到小尺寸物体的图像的细微特征。     

一种新的基于多小波的图像去噪算法

图像去噪是最基本的图像处理技术,小波分析是目前国际上最新的时间．频率分析工具,是信号去噪的强有力处理工具。通过小波变换可以将交织在一起的混合信号分解成不同频率的块信号。多小波所拥有的对称性、正交性、有限支撑等重要特性弥补了单小波的不足。本文提出了一种新的图像去噪方法,该方法以多小波变换为基础。实验证明,具有很好的去噪效果。     

3D shape recovery from image focus using kernel regression in eigenspace

Shape from focus (SFF) is one of the optical passive methods for three dimensional (3D) shape recovery of an object from its two dimensional (2D) images. The focus measure plays important role in SFF algorithms. Mostly, conventional focus measures are based on gradient, so their performance is restricted under noisy conditions. Moreover, SFF methods also suffer from loss of focus information due to discreteness. This paper introduces a new SFF method based on principal component analysis (PCA) and kernel regression. The focus values are computed through PCA by considering a sequence of small 3D neighborhood for each object point. We apply unsupervised regression through Nadaraya and Watson Estimate (NWE) on depth values to get a refined 3D shape of the object. It reduces the effect of noise within a small surface area as well as approximates the accurate 3D shape by exploiting the depth dependencies in the neighborhood. Performance of the proposed scheme is investigated in the presence of different types of noises and textured areas. Experimental results demonstrate effectiveness of the proposed approach.     

A new algorithm for the embedding of a prediction mechanism into the JPEG2000 coding chain

In this paper, we propose the embedding of a prediction mechanism into a part of the coding structure of JPEG2000 image compression standard, in order to reduce the amount of bits sent to the arithmetic coder, without any significant changes into the standard architecture and without loosing performance. The prediction is based upon an innovative processing of the data structures used by the standard JPEG2000 in progressive coding and the addition of a Prediction Matrix, whose computation does not add any overhead at the decoder side. Experiments are performed to test the efficacy of the prediction mechanism, and results are compared to the standard JPEG2000 and other similar approaches. Tests are documented over a set of well-known images from literature, also against different kinds of added noise. Performance, in terms of saved bits are reported, and a new figure of merit is defined to test the efficiency of Prediction. The results prove that the new proposal overcomes the standard and other related approaches for the entire set of referenced images, with significant gain in synthetic images, also in presence of noise.     

一种基于小波变换的多分辨图像融合算法

图像的融合过程是将两个或更多的图像结合成一个图像,融合图像保留了每一个图像的重要特征。图像融合技术能够提高图像质量和数据的实用性。主要描述了图像融合的两个课题：（1）融合算法;（2）质量评估。介绍的图像融合算法是先对原图像进行多分辨小波变换,再把输入图像的小波变换系数通过一定的融合规则进行选择,然后经过逆小波变换得到新的图像,对图像融合的质量评估也进行了讨论。     

无下采样轮廓小波在图像融合中的应用

在无下采样Contourlet变换和小波变换的基础上,针对低、高频系数的特点,提出一种新的基于无下采样轮廓小波变换的图像融合算法。该算法对无下采样轮廓小波分解后的低频部分采用了选择与加权平均的融合规则进行融合,而对各层高频系数采用局部区域加权平均融合规则进行融合。实验结果表明,该方法在包含信息量、清晰度上都有明显的提高。