Microarray image enhancement by denoising using stationary wavelet transform

Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.     

图像对比度增强的非线性变换法

对原始图像用所提出的判据判断图像的对比度类型,针对不同类型直接确定灰度变换参数,实现图像全局对比度增强。再对图像进行离散平稳小波变换,利用所提出的非线性增强方法分别对各个分解层的高频子带进行细节增强。实验表明,提出的方法在有效提高图像整体对比度的同时,又能突出图像中目标的细节部分信息。     

OCT skin image enhancement through attenuation compensation

The enhancement of optical coherence tomography (OCT) skin images can help dermatologists investigate the morphologic information of the images more effectively. In this paper, we propose an enhancement algorithm with the stages that includes speckle reduction, skin layer detection, and attenuation compensation. A weighted median filter is designed to reduce the level of speckle while preserving the contrast. A novel skin layer detection technique is then applied to outline the main skin layers: stratum corneum, epidermis, and dermis. The skin layer detection algorithm does not make any assumption about the structure of the skin. A model of the light attenuation is then used to estimate the attenuation coefficient of the stratum corneum, epidermis, and dermis layers. The performance of the algorithm has been evaluated qualitatively based on visual evaluation and quantitatively using two no-reference quality metrics: signal-to-noise ratio and contrast-to-noise ratio. The enhancement algorithm is tested on 35 different skin OCT images, which show significant improvements in the quality of the images, especially in the structures at deeper levels.     

Efficient fringe image enhancement based on dual-tree complex wavelet transform

In optical phase shift profilometry (PSP), parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest in real time three-dimensional (3D) modeling applications because it enables 3D reconstruction using just a few image captures. When using this approach in a real life environment, however, the noise in the captured images can greatly affect the quality of the reconstructed 3D model. In this paper, a new image enhancement algorithm based on the oriented two-dimenional dual-tree complex wavelet transform (DT-CWT) is proposed for denoising the captured fringe images. The proposed algorithm makes use of the special analytic property of DT-CWT to obtain a sparse representation of the fringe image. Based on the sparse representation, a new iterative regularization procedure is applied for enhancing the noisy fringe image. The new approach introduces an additional preprocessing step to improve the initial guess of the iterative algorithm. Compared with the traditional image enhancement techniques, the proposed algorithm achieves a further improvement of 7.2 dB on average in the signal-to-noise ratio (SNR). When applying the proposed algorithm to optical PSP, the new approach enables the reconstruction of 3D models with improved accuracy from 6 to 20 dB in the SNR over the traditional approaches if the fringe images are noisy.     

Selective image enhancement using attribute morphology

Abstract

Numerous applications in image interpretation involve feature extraction. In many low-contrast images, this process is pre-empted by an image enhancement phase to improve the quality of features and accentuate fine details. Few attempts at image enhancement have focused on a selective approach, whereby features are enhanced based on their characteristics. This technical note presents preliminary work on a novel image enhancement algorithm based on attribute morphology. The algorithm is amorphous, that is, it does not rely on the shape of a neighbourhood operator, but rather on a feature attribute such as area or contrast. The algorithm can be directed towards differently shaped structures simultaneously without undue noise amplification. The algorithm is demonstrated through the enhancement of features in various biological images.     

A novel brain image enhancement method based on nonsubsampled contourlet transform

In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.     

非线性小波变换在故障特征提取中的应用

经典小波变换在不同尺度采用一种小波基 ,不能很好地匹配信号的局部特征 ,因而造成降噪信号丢失了原始信号中部分的有用信息。为了克服上述缺陷 ,提出了一种基于第二代小波变换的非线性小波变换振动信号预处理方法 .应用第二代小波变换的预测器和更新器相互独立的特点 ,根据预测方差最小的选取原则 ,确定每个变换样本的最佳预测器 ,使预测器能够适应信号的局部特征。模拟数据和振动信号的分析表明 ,该方法克服了传统小波降噪方法局部信息丢失的缺陷 ,不仅可以有效地去除信号中的噪声 ,而且能够保留信号的局部特征。作为一种预处理方法 ,在某发电厂的故障诊断中有效地从振动信号中提取了故障特征。     

Three dimension reconstruction through measure-based image selection

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.     

Multifocus noisy image fusion using contourlet transform

Abstract

Image fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the presence of different types of noise. Performance of the proposed method is compared with principal components analysis and sharp fusion based methods as well as other fusion methods based on variants of wavelet transform like dual tree complex wavelet transform, discrete wavelet transform, lifting wavelet transform, multiwavelet transform, stationary wavelet transform and pyramid transform using six standard quantitative quality metrics (entropy, standard deviation, edge strength, fusion factor, sharpness and peak signal to noise ratio). The combined qualitative and quantitative evaluation of the experimental results shows that the proposed method performs better than other methods.     

Investigation of fatigue crack closure using multiscale image correlation experiments

Two full-field macroscale methods are introduced for estimating fatigue crack opening levels based on digital image correlation (DIC) displacement measurements near the crack tip. Crack opening levels from these two full-field methods are compared to results from a third (microscale) method that directly measures opening of the crack flanks immediately behind the crack tip using two-point DIC displacement gages. Of the two full-field methods, the first one measures effective stress intensity factors through the displacement field (over a wide region behind and ahead of the crack tip). This method reveals crack opening levels comparable to the limiting values (crack opening levels far from the crack tip) from the third method (microscale). The second full-field method involves a compliance offset measurement based on displacements obtained near the crack tip. This method delivers results comparable to crack tip opening levels from the microscale two-point method. The results of these experiments point to a normalized crack tip opening level of 0.35 for R ∼ 0 loading in grade 2 titanium. This opening level was found at low and intermediate ΔK levels. It is shown that the second full-field macroscale method indicates crack opening levels comparable to surface crack tip opening levels (corresponding to unzipping of the entire crack). This indicates that effective stress intensity factors determined from full-field displacements could be used to predict crack opening levels.     

Automated image enhancement using power law transformations

We propose a scheme for automating power law transformations which are used for image enhancement. The scheme we propose does not require the user to choose the exponent in the power law transformation. This method works well for images having poor contrast, especially to those images in which the peaks corresponding to the background and the foreground are not widely separated.     

Video enhancement through image registration based on structural similarity

Abstract

It is commonly known that the mean square error (MSE) does not accurately reflect the subjective image quality for most video enhancement tasks. Among the various image quality metrics, structural similarity (SSIM) metric provides remarkably good prediction of the subjective scores. In this paper, a new registration method based on contribution of structural similarity measurement to the well known Lucas–Kanade (LK) algorithm has been proposed. The core of the proposed method is contributing the SSIM in the sum of squared difference of images along with the Levenberg–Marquardt optimisation approach in LK algorithm. Mathematical derivation of the proposed method, based on the unified framework of Baker et al., is given. The proposed registration algorithm is applied to a video enhancement successfully. Various objective and subjective comparisons show the superior performance of the proposed method.     

SVD-based digital image watermarking using complex wavelet transform

A new robust method of non-blind image watermarking is proposed in this paper. The suggested method is performed by modification on singular value decomposition (SVD) of images in Complex Wavelet Transform (CWT) domain while CWT provides higher capacity than the real wavelet domain. Modification of the appropriate sub-bands leads to a watermarking scheme which favourably preserves the quality. The additional advantage of the proposed technique is its robustness against the most of common attacks. Analysis and experimental results show much improved performance of the proposed method in comparison with the pure SVD-based as well as hybrid methods (e.g. DWT-SVD as the recent best SVD-based scheme).     

QR code-based non-linear image encryption using Shearlet transform and spiral phase transform

In this paper, we propose a new quick response (QR) code-based non-linear technique for image encryption using Shearlet transform (ST) and spiral phase transform. The input image is first converted into a QR code and then scrambled using the Arnold transform. The scrambled image is then decomposed into five coefficients using the ST and the first Shearlet coefficient, C₁ is interchanged with a security key before performing the inverse ST. The output after inverse ST is then modulated with a random phase mask and further spiral phase transformed to get the final encrypted image. The first coefficient, C₁ is used as a private key for decryption. The sensitivity of the security keys is analysed in terms of correlation coefficient and peak signal-to noise ratio. The robustness of the scheme is also checked against various attacks such as noise, occlusion and special attacks. Numerical simulation results are shown in support of the proposed technique and an optoelectronic set-up for encryption is also proposed.     

双正交小波变换多分辨率图像融合方法

为了避免常规正交小波变换所引起的图像边缘失真,特别采用了双正交小波变换进行图像的多分辨率分解。在融合处理时,为了获得更丰富的细节和更好的融合效果,对不同分解层、不同方向上的高频分量以及同一分解层图像中的不同区域均采用不同的融合算子。实验结果表明,该融合方法和融合规则是十分有效的,融合后图像的边缘和纹理细节都更加突出。     

Deep underwater image enhancement through colour cast removal and optimization algorithm

ABSTRACT

Blue–green colour cast effect and low contrast are common problems suffered by deep underwater images. This paper introduces a new method which consists of two major steps: red channel correction based on green and blue channels (RCCGB), and simultaneous contrast stretching and mean pixel enhancement (SCSMPE). The RCCGB is designed to minimize the effect of blue–green illumination. This step considers the differences between the red channel and other channels in terms of total pixel values. The second major step, SCSMPE is specifically designed to perform contrast stretching and improve the mean pixel value simultaneously through particle swarm optimization (PSO). Based on the visual observation, the proposed method significantly reduces the effect of the blue–green colour cast and improves the image contrast. Furthermore, the average quantitative values for 300 underwater images also demonstrate the superiority of the proposed method.     

应用分数傅立叶变换方法的水下目标特征提取

针对基于传统时频分析方法的水下目标特征提取方法的缺陷,引入了一种新的时频分析方法——分数阶傅立叶变换（FRFT）方法用于处理水下目标特征提取问题。仿真信号和实际试验数据的处理结果表明,当主动声纳的发射信号为线性调频信号时,分数阶傅立叶变换方法可以使目标的回波能量在分数阶域的对应区域聚集,使目标和混响的分数阶傅立叶变换在分数阶域上不仅呈现明显不同的特征,而且较其他时频分析方法具有抗混响的优点。     

Visibility enhancement of fog-degraded image using adaptive total variation minimisation

Images degraded due to bad weather conditions affect the quality of vision-based security systems, as the distortions obscure contrasts in the frames of the images. In this paper, we present a novel approach that uses adaptive total variation minimisation to retain the object and reduce the noise from a single image, thereby enhancing fog-degraded images. Representative experimental results show that our proposed algorithm is effective for contrast and saturation enhancement of fog-degraded images.     

阵列信号维纳滤波用于主动声呐图像增强处理

声呐图像的噪声背景抑制是提高水下目标检测能力的重要问题.针对声呐图像背景斑点噪声强、目标轮廓模糊、目标回波对比度低等问题,利用确定性目标回波信号与随机分布的干扰噪声之间的相关统计特性差异,采用基于最小均方差准则的阵列信号维纳滤波器,通过主动最小方差无畸变响应(Minimum Variance Distortionles...