光学相干层析成像（OCT）的图像质量会受到散斑噪声的影响，限制了OCT在临床诊断中的应用。叠加取平均是一种降噪的常用方法，作为相干噪声，散斑降噪的关键在于降低用于叠加的图像之间的散斑相关度。提出了一种基于纯随机相位板的散斑去相关OCT系统（PSD-OCT）。利用纯随机相位板调制样品光的波前相位以实现散斑去相关，从而为采用叠加取平均降低OCT 图像噪声的方法提供低相关数据。成像实验证明了纯随机相位板能够降低散斑相关度，提升叠加图像的散斑信噪比，使得生物样品的精细结构更为清晰明显。与传统OCT的相关叠加相比，PSD-OCT的去相关叠加可以大幅降低散斑噪声从而增强OCT图像的视觉可见性，且无需搭建复杂系统，具有广泛的生物医学成像应用前景。

干涉测量中, 环境和成像系统热噪声以及不均匀的被测表面反射率均会使测量相位分布不一致, 以留数点的形式出现在相位包裹图中。在此情况下, 获得完整真实、高精度的被测表面相位解包裹结果就成为亟需解决的难题。不同于传统方法利用枝切连接留数点, 隔离噪声干扰区域; 或以丢失细节信息为代价, 采用最小范数类方法的解包裹方案,首次将和谐均值这一数论概念应用在相位解包裹中。方法利用其具有剔除粗大误差的特殊性质, 类比环绕留数点的四邻域相位点为待计算和谐均值数据, 直接检测噪声干扰点, 得到全局相位解包裹结果。最后利用强噪声干扰下, 复杂表面的计算机模拟实验, 通过定量定性分析, 对其结果精度, 运算效率, 简单易执行的优点进行了有效验证。

Analytical formulas and experimental proof of the echo broadening effect in active range-gated imaging, including atmospheric interference, currently exist. We investigate the impact of this effect on target detection. Our research demonstrates that the echo broadening effect affects the energy profile of the depth of view and collects only part of the signals of targets in head and tail zones. Under bad weather conditions, the effect weakens the signal-to-noise ratio (SNR) of images, especially in cases with large laser pulse width. Fortunately, by modifying the laser pulse width, the effect can be controlled. These results are valuable to the applications of active range-gated imaging.

In this paper, an image processing method for improving the quality of optical coherence tomography (OCT) images is proposed. Wavelet denoising based on context modeling and contrast enhancement by means of the contrast measure in the wavelet domain is carried out on the OCT images in succession. Three parameters are selected to assess the effectiveness of the method. It is shown from the results that the proposed method can not only enhance the contrast of images, but also improve signal-to-noise ratio. Compared with two other typical algorithms, it has the best visual effect.

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

A new method based on gray-natural logarithm ratio bilateral filtering is presented for image smoothing in this work. A new gray-natural logarithm ratio range filter kernel, leading to adaptive magnitude from image gray distinction information, is pointed out for the bilateral filtering. The new method can not only well restrain noise but also keep much more weak edges and details of an image, and preserve the original color transition of color images. Experimental results show the effectiveness for image denoising with our method.

A new locally adaptive image denoising method, which exploits the intra-scale and inter-scale dependency in the dual-tree complex wavelet domain, is presented. Firstly, a recently emerged bivariate shrinkage rule is extended to a complex coefficient and its neighborhood, the corresponding nonlinear threshold functions are derived from the models using Bayesian estimation theory. Secondly, an adaptive weight, which is able to capture the inter-scale dependency of the complex wavelet coefficients, is combined to the obtained bishrink threshold. The experimental results demonstrate an improved denoising performance over related earlier techniques both in peak signal-to-noise ratio (PSNR) and visual effect.

Eigenface method used in face recognition is introduced to reduce the pattern of interference fringes appearing in the absorption image of cold rubidium atom cloud trapped by an atom chip. The standard method for processing the absorption image is proposed, and the origin of the interference fringes is analyzed. Compared with the standard processing method which uses only one reference image, we take advantage of fifty reference images and reconstruct a new reference image which is more similar to the absorption image than all of the fifty original reference images. Then obvious reduction of interference fringes can be obtained.

Two image denoising approaches based on wavelet neural network (WNN) optimized by particle swarm optimization (PSO) are proposed. The noisy image is filtered by the modified median filtering (MMF). Feature values are extracted based on the MMF and then normalized in order to avoid data scattering. In approach 1, WNN is used to tell those uncorrupted but filtered by MMF and then the pixels are restored to their original values while other pixels will retain. In approach 2, WNN distinguishes the corrupted pixels and then these pixels are replaced by MMF results while other pixels retain. WNN can be seen as a classifier to distinguish the corrupted or uncorrupted pixels from others in both approaches. PSO is adopted to optimize and train the WNN for its low requirements and easy employment. Experiments have shown that in terms of peak signal-to-noise ratio (PSNR) and subjective image quality, both proposed approaches are superior to traditional median filtering.