基于X射线和结构光相机的煤矸石分拣方法研究

为了提高煤炭产量,减少污染物排放,在煤矿生产中必须进行煤矸石分离.常用的双能R值方法受物料厚度影响较大,无法满足大范围厚度的煤矸石分选需求.针对该问题,提出了一种联合R值曲线拟合和物料厚度信息的煤矸石识别方法.建立煤和矸石的低能透射信号值和R值的关系散点图,探究R值算法的适用范围和极限厚度.首先利用R值算法进行初步识别,再通过结构光相机获取R值相对应的厚度信息,与极限厚度比较后进一步识别煤矸石.最后计算识别为煤的部分占整个物体的比率,通过所求占比实现煤矸石的分选.实验结果证明,该方法可以识别50 mm~300 mm范围的煤矸石,煤的最低识别率大于80%,矸石的最高识别率低于10%,整体识别准确率高于96%.

基于分区域处理的低剂量CT重建算法

为解决由于过度的量子噪声使低剂量CT重建图像质量产生退化的问题,提出一种基于分区域处理的联合先验低剂量CT统计迭代重建算法。对重建过程中的图像进行区域划分,对图像进行中值滤波并计算滤波图像的梯度,根据梯度划分出图像的边缘区域和平坦区域,分别利用全变分正则化(TV)和高斯马尔可夫随机场(MRF)正则化对不同的区域进行惩罚,将这两种正则项作为联合先验应用到惩罚加权最小二乘重建算法中,使用超松弛迭代算法(SOR)对目标函数进行求解。仿真结果表明,该算法去噪能力强,能有效保护重建图像的边缘细节信息。

基于结构张量的自适应稀疏角度CT重建算法

针对全变差稀疏角度CT重建图像出现伪影及过渡平滑的问题,提出一种自适应方向全变差与Lp范数结合的稀疏重建算法.算法利用边缘细化改善结构张量边缘检测精度,然后基于结构张量提取的结构方向信息,自适应地更新方向全变差中的方向及权重.最后采用交替方向乘子法,结合广义软阈值算法求解.实验结果显示,所提算法在主观评价与客观评价上均具有优势,有效抑制了条形伪影,较好地保持了图像边缘,进而提高了稀疏重建图像的质量.

紧框架小波和总广义全变分联合约束的医学图像复原算法

为了克服传统全变分正则化方法容易造成复原图像中出现阶梯状伪边缘、纹理细节丢失的不足,本文提出了一种紧框架小波和总广义全变分联合约束的图像复原算法.首先,结合紧框架小波能够捕获含噪声或退化图像中的奇异点的优势,同时采用能够逼近任意阶多项式函数进而可以保留图像尖锐边缘的总广义全变分,构造出一种由紧框架小波的L_1范数和二阶总广义全变分的L_2范数组成的联合正则项约束的图像复原模型;其次,采用交替方向迭代方法将所提模型的最小化问题分解为两个子问题,并分别采用均值增广拉格朗日算法和Chambolle-Pock一阶原始—对偶迭代方法获得最优解.实验结果表明,所提算法在抑制噪声的同时能够有效复原图像的边缘、细节信息,两种量化指标峰值信噪比和结构相似度的值也能直观体现复原图像质量的提高水平.

基于正弦图恢复的CT局部重建算法

针对局部扫描过程中投影数据沿探测器方向截断的特点,提出一种基于正弦图数据恢复的扇束CT局部重建算法.算法首先基于截断正弦图提取的边缘信息对缺失投影数据进行扩充,接下来对未扩充完整的零值区域利用两侧已知的投影数据进行线性插值,最后依据扩充完整的投影数据经滤波反投影重建出局部图像.仿真和临床数据的重建结果表明,算法对直接利用局部截断投影数据重建图像中呈现的环形伪影有明显的抑制作用,且重建图像边缘清晰.

基于正弦图分区修复的稀疏角度CT重建算法

针对稀疏投影CT重建图像中的条形伪影问题,提出一种稀疏表示与低秩矩阵填充相结合的正弦图分区修复方法。首先,将正弦图子块依据灰度熵大小分为两类;然后,采用字典学习算法修复边界区域的正弦图子块,为了保留正弦图的内部结构,设计一种联合修复模型用于内部子块的修复,将正弦图的低秩特性融入稀疏表示模型中,以便引入非局部信息;最后,组成完整的正弦图并经滤波反投影(FBP)重建获得最终图像。实验结果表明,与经典算法相比,该算法在投影域与图像域皆有较优表现,能够较好地修复正弦图的结构,明显改善稀疏重建图像中的条形伪影及结构模糊问题。

基于先验图像约束压缩感知多能CT重建算法

由不同材质构成且材质之间密度相差较大的工件进行单一能量CT重建时无法获取完整内部结构.为获得结构信息完整的高质量CT重建图像,研究了基于先验图像压缩感知多能重建方法.首先从低到高依次采集多个能量下的投影数据,并用凸集投影-全变分最小化(POCS-TVM)算法对最低能量的投影数据进行CT重建,然后,将重建好的图像作为先验信息,利用先验图像压缩感知算法(PICCS)对下一组能量下的投影进行重建,重建后的图像再次作为新的先验信息重复以上步骤,依此类推直到最高能量的数据重建,以此达到完整重建.结果表明该方法可以有效减少因投影缺失而导致的伪影并保护低密度边缘.

Local edge direction based non-local means for image denoising

Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.

Minimum variance adaptive beamforming combined with coherence factor weighting applied to ultrafast active cavitation imaging

The ultrafast active cavitation imaging(UACI)based on plane wave transmission and delay-and-sum(DAS)beamforming has been developed to monitor cavitation events with a high frame rate.However,DAS beamforming leads to images with limited resolution and contrast.In this paper,minimum variance(M V)adaptive beamforming and coherence factor(CF)weighting are combined to achieve an MVCF-based UACI,which can improve the cavitation imaging quality.The detailed algorithm evaluation has been investigated from both simulation and experimental data The simulation data include10point targets and a cyst,while the experimental data are obtained by detecting the dissipation of cavitation bubbles in water excited by a single element transducer with frequency of1.2MHz.The advantages of the proposed methodology as well as the comparison with conventional B-mode,DAS?M V,DAS-CF and MV on the basis of compressive sensing(CS)(called MVCS)beamformers are discussed.The results show that MVCF beamformer has a significant improvement in terms of both resolutions and signal-to-noise ratio(SN R).The MVCF-based UACI has a SNR at21.82dB higher,lateral and axial resolution at2.69times and1.93times?respectively,which were compared with those of B-mode active cavitation mapping.The MVCF-based UACI can be used to image the residual cavitation bubbles with a higher SNR and better spatial resolution

Direct and real-time sub-wavelength resolution photoacoustic imaging method based on acoustic lens with negative refractive index

The signal processing technology based on material with negative refractive index provides researchers with the latest ideas. As a new nondestructive bio-photonic technology, photoacoustic tomography is a kind of imaging method based on the differences of optical absorption within the biological organization However, photoacoustic tomography by the scanning sensor or by the sensors array at present has its inherent disadvantages that may lead to poor real-time performance and high cost in the imaging process. The characteristics of acoustic lens with negative refractive index such as focusing, filtering and directional control on acoustic wave, are very suitable for solving the problem in photoacoustic tomography. With an analysis on the nega-tive quality response of acoustic lens and the advantages of negative refractive imaging, we proposed an approach using the lens to change the current photoacoustic imaging methods. The experiment showed that the imaging effectiveness of photoacoustic tomography by the designed lens is very impressive that the pressure distribution of the absorber is basically consistent with the image of the absorber. In addition, the result of 0. 6 times wavelength in the experimental image is demonstrated on sub-wave-length photoacoustic imaging through the lens designed in this work.