A color correction algorithm for noisy multi-view images

A novel color correction algorithm for noisy multi-view images is presented. The key idea is to use the improved Karhunen-Loeve (K-L) transform to obtain correction matrix that can eliminate noise effect to the fullest extent. Noise variance estimation is first performed in the algorithm. In the end, wavelet transform is applied to denoise the corrected image. Experimental results show that, compared with traditional correction method, a well-performed correction result is achieved using the proposed method,and the visual effect of the denoised corrected image is almost consistent with ideal corrected image.     

Balanced multi-image demons for non-rigid registration of magnetic resonance images

A new approach is introduced for non-rigid registration of a pair of magnetic resonance images (MRI). It is a generalization of the demons algorithm with low computational cost, based on local information augmentation (by integrating multiple images) and balanced implementation. Specifically, a single deformation that best registers more pairs of images is estimated. All these images are extracted by applying different operators to the two original ones, processing local neighbors of each pixel. The following five images were found to be appropriate for MRI registration: the raw image and those obtained by contrast-limited adaptive histogram equalization, local median, local entropy and phase symmetry. Thus, each local point in the images is supplemented by augmented information coming by processing its neighbor. Moreover, image pairs are processed in alternation for each iteration of the algorithm (in a balanced way), computing both a forward and a backward registration.The new method (called balanced multi-image demons) is tested on sagittal MRIs from 10 patients, both in simulated and experimental conditions, improving the performances over the classical demons approach with minimal increase of the computational cost (processing time around twice that of standard demons). Specifically, a simulated deformation was applied to the MRIs (either original or corrupted by additive Gaussian or speckle noises). In all tested cases, the new algorithm improved the estimation of the simulated deformation (squared estimation error decreased by about 65% in the average). Moreover, statistically significant improvements were obtained in experimental tests, in which different brain regions (i.e., brain, posterior fossa and cerebellum) were identified by the atlas approach and compared to those manually delineated (in the average, Dice coefficient increased of about 6%).The conclusion is that a balanced method applied to multiple information extracted from neighboring pixels is a low cost approach to improve registration of MRIs.     

A novel, fast entropy-minimization algorithm for bias field correction in MR images

A novel, fast entropy-minimization algorithm for bias field correction in magnetic resonance (MR) images is suggested to correct the intensity inhomogeneity degradation of MR images that has become an increasing problem with the use of phased-array coils. Four important modifications were made to the conventional algorithm: (a) implementation of a modified two-step sampling strategy for stacked 2D image data sets, which included reducing the size of the measured image on each slice with a simple averaging method without changing the number of slices and then using a binary mask generated by a histogram threshold method to define the sampled voxels in the reduced image; (b) improvement of the efficiency of the correction function by using a Legendre polynomial as an orthogonal base function polynomial; (c) use of a nonparametric Parzen window estimator with a Gaussian kernel to calculate the probability density function and Shannon entropy directly from the image data; and (d) performing entropy minimization with a conjugate gradient method. Results showed that this algorithm could correct different types of MR images from different types of coils acquired at different field strengths very efficiently and with decreased computational load.     

A correction algorithm for undersampled images using dynamic segmentation and entropy based focus criterion

A post-processing technique is presented for correcting images undersampled in k-space. The method works by taking advantage of the image's background zeros (dynamically segmented through the application of a threshold) to extrapolate the missing k-space samples. The algorithm can produce good quality images from a small set of k-space frequencies with only a few iterations of simple matrix operations, using the image entropy as the focus criterion. It does not require any special patient preparation, extra pulse sequences, complex gradient programming or specialized hardware. This makes it a good candidate for any application that requires short scan times or where only few frequencies can be sampled.     

An improved fusion algorithm for infrared and visible images based on multi-scale transform

In this paper, an improved fusion algorithm for infrared and visible images based on multi-scale transform is proposed. First of all, Morphology-Hat transform is used for an infrared image and a visible image separately. Then two images were decomposed into high-frequency and low-frequency images by contourlet transform (CT). The fusion strategy of high-frequency images is based on mean gradient and the fusion strategy of low-frequency images is based on Principal Component Analysis (PCA). Finally, the final fused image is obtained by using the inverse contourlet transform (ICT). The experiments and results demonstrate that the proposed method can significantly improve image fusion performance, accomplish notable target information and high contrast and preserve rich details information at the same time.     

Non-uniformity correction algorithm for IRFPA based on motion controllable micro-scanning and perimeter diaphragm strips

Non-uniformity correction is the key issue for the image quality improvement of infrared focal panel array (IRFPA) imaging. A non-uniformity correction (NUC) algorithm for IRFPA based on motion controllable micro-scanning platform and perimeter diaphragm strips is presented. We initially execute one-point calibration to the perimeter detectors, then based on controllable motion of adjacent frames, a special algebraic algorithm is proposed to transport the calibration of the perimeter detectors to those interior un-corrected ones. In this way, the bias parameter of the whole field of view (FOV) is calculated. The algorithm can be easily combined with sub-pixel imaging, thereby improving the quality of thermal imaging system (image spatial resolution and uniformity). All calculations are algebraic, with a low computation load. The algorithm can realize adaptive one point calibration without covering the central FOV rapidly. Experiments on simulated infrared data demonstrate that this algorithm requires only dozens of frames to obtain high quality corrections.     

Corners detection on finger vein images using the improved Harris algorithm

An improved Harris corner detection algorithm is proposed based on Barron operator, since Harris corner detection algorithm has a poor accuracy in positioning complex corner detection and may miss certain real corners. Firstly, the image gradient is calculated by using Barron operator to reduce the calculation errors from Prewitt operator or Sobel operator. Secondly, the centre B-spline function is used to smooth image, filter noise, and retain the corners information better. Thirdly, a non-maximal inhibition and corners sieving method is used to determine whether the detected corners are real corners or not. A square window is centered at the pixel and eliminate the corner if the value of the corner response function is non-maximal in the window. And then divide the test image into several blocks so as to process each block independently, and use a cyclic iterative method to determine the threshold value to make sure that the real corners are accurately selected. Finally, experiments indicate the algorithm has relatively great noise proof ability and is able to extract complex corners effectively.     

一种改进的RGB Hough车牌校正定位算法

车牌的定位对车牌识别的成功率起到决定性作用。为了通过校正倾斜的车牌提高车牌在倾斜情况下字符识别的准确率,针对小型车蓝底白字的车牌提出了一种基于RGB色彩空间与Hough变换的车牌校正定位算法。对无倾斜情况的车牌可以直接定位,对于存在明显倾斜的车牌可以实现倾斜校正并定位。对车牌无倾斜情况的定位准确率为93%,倾斜车牌的定位准确率为88%。     

一种改进的RGB Hough车牌校正定位算法

车牌的定位对车牌识别的成功率起到决定性作用。为了通过校正倾斜的车牌提高车牌在倾斜情况下字符识别的准确率,针对小型车蓝底白字的车牌提出了一种基于RGB色彩空间与Hough变换的车牌校正定位算法。对无倾斜情况的车牌可以直接定位,对于存在明显倾斜的车牌可以实现倾斜校正并定位。对车牌无倾斜情况的定位准确率为93%,倾斜车牌的定位准确率为88%。     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

Study on an improved five-interferogram phase-shifting algorithm

Using traditional five-interferogram algorithm to unwrap phase for length measurement, the phase steps must be equal to π/2 exactly, but it is almost impossible to achieve in nanometer positioning technique.Aiming to overcome this defect of traditional five-interferogram algorithm, an improved five-interferogram algorithm is presented.This improved algorithm not only keeps the high accuracy of traditional five-interferogram algorithm, but also does not need absolute equal step to unwrap phase.Instead, this algorithm only needs measuring phase-shifting.With the numerical simulation, the improved five-interferogram algorithm shows high accuracy, high reliability, and feasibility in practice.It is very valuable for accurate length measurement with Fizeau interferometer and Fabry-Perot interferometer.     

Study on an improved phase-shifting five-interferogram algorithm

Using traditional five-interferogram algorithm to unwrap phase for length measurement, the phase steps must be equal to π/2 exactly, but it is almost impossible to achieve in nanometer positioning technique. Aiming to overcome this defect of traditional five-interferogram algorithm, an improved five-interferogram algorithm is presented. This improved algorithm not only keeps the high accuracy of traditional fiveinterferogram algorithm, but also does not need absolute equal step to unwrap phase. Instead, this algorithm only needs measuring phase-shifting. With the numerical simulation, the improved five-interferogram algorithm shows high accuracy, high reliability, and feasibility in practice. It is very valuable for accurate length measurement with Fizeau interferometer and Fabry-Perot interferometer.     

工业CT图像边缘伪影校正

为了去除工业CT图像中的边缘伪影,提高CT图像的识别能力和尺寸测量精度,提出一种降低串扰的系数修正法。首先分析得出边缘伪影主要是由散射射线在相邻探测通道之间的串扰所导致,并给出了探测通道串扰的数学模型;然后根据数学模型设计实验方案,通过对影响串扰率的主要因素进行实验分析,得到串扰率随入射X射线能量和被测物体厚度变化关系,再通过最小二乘拟合得出投影数据关于串扰率的函数;最后利用此函数对投影数据进行校正,降低了串扰的影响。实验结果表明,探测器间一级串扰率约为9.0%,二级串扰率约为1.2%,其中一级串扰是造成边缘伪影的主要因素,采用本文方法能够有效地抑制边缘伪影,同时较好地保留了图像细节和边缘。     

工业CT图像边缘伪影校正

为了去除工业CT图像中的边缘伪影,提高CT图像的识别能力和尺寸测量精度,提出一种降低串扰的系数修正法。首先分析得出边缘伪影主要是由散射射线在相邻探测通道之间的串扰所导致,并给出了探测通道串扰的数学模型;然后根据数学模型设计实验方案,通过对影响串扰率的主要因素进行实验分析,得到串扰率随入射X射线能量和被测物体厚度变化关系,再通过最小二乘拟合得出投影数据关于串扰率的函数;最后利用此函数对投影数据进行校正,降低了串扰的影响。实验结果表明,探测器间一级串扰率约为9.0%,二级串扰率约为1.2%,其中一级串扰是造成边缘伪影的主要因素,采用本文方法能够有效地抑制边缘伪影,同时较好地保留了图像细节和边缘。     

Developing an algorithm for compression, multiplexing and enhancement of multiple images

Image compression is one of the important fields that has useful applications in data storage and transmission. In this research a new algorithm is developed and tested for multiple-image compression and enhancement. The algorithm, in addition, is applied to multiple noisy images. Also, the effect of compression ratio on the peak signal to noise ratio (PSNR) is explored by applying different compression ratios. The developed algorithm gives good compression and noise immunity. It can be used for storage/transmission of encrypted and compressed information.     

An algorithm for encryption of secret images into meaningful images

Image encryption algorithms typically transform a plain image into a noise-like cipher image, whose appearance is an indication of encrypted content. Bao and Zhou [Image encryption: Generating visually meaningful encrypted images, Information Sciences 324, 2015] propose encrypting the plain image into a visually meaningful cover image. This improves security by masking existence of encrypted content. Following their approach, we propose a lossless visually meaningful image encryption scheme which improves Bao and Zhou's algorithm by making the encrypted content, i.e. distortions to the cover image, more difficult to detect. Empirical results are presented to show high quality of the resulting images and high security of the proposed algorithm. Competence of the proposed scheme is further demonstrated by means of comparison with Bao and Zhou's scheme.     

A method for automatic correction of drift-distorted SPM images

A method for automatic correction of drift-distorted surface images obtained using a scanning probe microscope is developed. For the case of slowly varying drifts, simple linear transformations describing the observed distortions are constructed. One or two pairs of counter-scanned images (CSIs) are used as initial data. A system of linear equations is solved by finding the coordinate of the same surface feature in each CSI. The solutions obtained, i.e., linear transformation coefficients, are used to correct drift-induced distortions in horizontal and vertical planes. Two nonlinear drift correction methods are proposed, which provide a better accuracy in comparison with the linear correction method. The developed method can be used in feature-oriented scanning of the surface.     

低能X射线工业CT图像杯状伪影校正

为了去除X射线工业CT图像中的杯状伪影,提高CT图像的识别能力和量化分析精度,提出一种基于分度投影和权函数的射束硬化校正方法。首先分析得出杯状伪影主要是由X射线连续谱穿过被测物体过程中出现的射束硬化所导致。然后扫描阶梯模型,采集不同厚度下的投影数据并求出线衰减系数,通过拟合曲线,得到硬化模型函数和权函数校正模型函数,并确定权函数。接着,扫描被测圆柱形工件,采集不同分度下的投影数据。最后,针对每一个分度投影数据,采用权函数与当前分度投影数据乘积的方法进行硬化校正。对含有杯状伪影的实际CT图像进行了校正实验,结果表明,与多项式拟合法相比,该方法校正后的灰度图像没有放大噪声,且信噪比提高3.29%,有效地消除了杯状伪影,同时较好地保留了图像边界细节。     

Dominant color extraction based color correction for multi-view images

Color information is very important in setting the style of images. In this paper, a color correction method based on dominant color extraction is proposed to eliminate the color inconsistence between multi-view images. With the theory of basic color categories, dominant colors from the categories are extracted for reference image and input image, and then the corresponding color mapping relationships are built.Experimental results show that the proposed method is quite effective.     

低能X射线工业CT图像杯状伪影校正

为了去除X射线工业CT图像中的杯状伪影,提高CT图像的识别能力和量化分析精度,提出一种基于分度投影和权函数的射束硬化校正方法。首先分析得出杯状伪影主要是由X射线连续谱穿过被测物体过程中出现的射束硬化所导致。然后扫描阶梯模型,采集不同厚度下的投影数据并求出线衰减系数,通过拟合曲线,得到硬化模型函数和权函数校正模型函数,并确定权函数。接着,扫描被测圆柱形工件,采集不同分度下的投影数据。最后,针对每一个分度投影数据,采用权函数与当前分度投影数据乘积的方法进行硬化校正。对含有杯状伪影的实际CT图像进行了校正实验,结果表明,与多项式拟合法相比,该方法校正后的灰度图像没有放大噪声,且信噪比提高3.29%,有效地消除了杯状伪影,同时较好地保留了图像边界细节。