一种数字视频自适应降噪算法的研究与实现

视频序列在传输过程中常常受到噪声的污染,致使画面效果变差,因此需要数字视频降噪方法来降低噪声对图像的影响,改善图像质量。本文简单介绍了数字视频降噪的原理,在现有的自适应降噪方法基础上提出一种改进的自适应数字降噪的实现算法,介绍了其硬件实现过程。和现有的降噪方法相比,本文提出的方法能有效的消除传统方法带来的拖尾等问题。噪声是影响目标信号检测和显示的主要因素之一,信号在经过纪录、编辑、分配、传输及卫星接收等各种途径都可能混入大量干扰,致使图像模糊,质量退化。如何有效去除视频序列中的不良影响一直是提高画质的主要问题所在。     

基于改进三维块匹配滤波的红外全息降噪算法北大核心CSCD

红外全息技术更适用于长距离的大视场成像,但其散斑噪声与高斯噪声对图像质量的影响也更加显著,限制了红外全息技术的应用与推广。本文通过引入全局傅立叶阈值与自适应维纳滤波的方法对三维块匹配滤波算法进行优化,提高了其对红外全息图像降噪的适应性与细节保留,得到改进的三维块匹配滤波算法,并与多种采用传统滤波方法的结果进行了对比。结果表明,改进后的算法可以在对红外全息图像中的高斯噪声等环境噪声与散斑噪声进行降噪的同时保留更多细节,是一种更加适用于红外全息图像的降噪方法。     

激光超声可视化图像处理研究

在激光超声可视化成像过程中,外界噪声、入射波等干扰导致了图像质量下降,造成了缺陷识别能力不足。利用局部统计滤波算法降低散斑噪声干扰,通过考察平均梯度和熵值以评估降噪效果。利用二阶微分算子对降噪后的图像进行图像增强,比较了不同微分算子的图像增强效果。提出利用罚函数方法抑制入射波对缺陷散射波的干扰,通过权重因子和正则化因子的变化考察了峰值信噪比和结构相似性的变化趋势,并与相邻波相减抑制入射波方法进行了对比。结果表明,通过图像处理方法可以改善激光超声可视化图像质量,突显缺陷信息。     

双通道深度图像先验降噪模型

相对于采用固定网络参数值的有监督深度降噪模型而言,无监督的深度图像先验（Deep Image Prior,DIP）降噪模型更具灵活性和实用性.然而,DIP模型的降噪效果远低于有监督降噪模型（尤其是在处理人工合成噪声图像时）.为进一步提升DIP降噪模型的降噪效果,本文提出了双通道深度图像先验降噪模型.该降噪模型由噪声图像预处理、在线迭代训练和图像融合3个模块组成.首先,利用BM3D和FFDNet两种经典降噪方法对给定的噪声图像进行预处理,得到2张初步降噪图像,然后,将原DIP单通道逼近目标图像架构拓展为双通道工作模式.其中,第一通道以FFDNet初步降噪图像和噪声图像为双目标图像,第二通道则以BM3D预处理图像和噪声图像为双目标图像.在此基础上,按照标准的DIP在线训练方式让DIP网络输出图像在两个通道上分别逼近各自的目标图像,同时依据基于边缘能量定义的伪有参考图像质量评价值适时终止迭代过程,从而获得2张中间生成图像.最后,使用结构化图块分解融合算法将两张中间生成图像融合并作为最终的降噪后图像.实验数据表明,在合成噪声图像上,本文提出的双通道深度图像先验降噪模型在各个噪声水平上显著优于原...     

基于SWBC变换尺度相关性的红外图像阈值降噪

针对传统图像降噪算法无法较好处理红外图像中的噪声问题,提出了一种基于SWBC(Stationary Wavelet-based Contourlet)变换尺度相关性的红外图像阈值降噪算法。本文分析红外图像信号和噪声在SWBC变换域各尺度上的能量分布特性,改进一般降噪算法对所有子带均进行处理的做法,只对高频子带系数进行降噪处理。同时为增加SWBC系数阈值判断的准确性,本文算法对每个系数设置不同的阈值,结合尺度相关特性,对系数进行双重判断。使用不同的含噪红外图像对本文算法进行检验。实验结果表明,相比于WBC尺度间硬阈值降噪、WBC尺度间自适应阈值降噪和WBC尺度相关性降噪,本文算法能获得更高的SNR提升,且SSIM值也更接近于1。     

模糊神经网络像素分类的稀疏表示医学CT图像去噪方法

在医学CT成像过程中,由于引入了不可避免的噪声,致使图像质量下降,影响临床诊断。因此,研究医学CT图像降噪方法在诊疗服务中具有重要意义。本文结合图像分割的思想,利用模糊神经网络将图像像素分成边缘区、平滑区与纹理区等不同图像区域,通过小波稀疏表示对不同类型的图像块进行阈值去噪处理,以便更好地保留医学CT图像的细节特征。实验结果表明,本文算法对医学CT图像降噪有一定的效果,峰值信噪比（PSNR）和结构相似性指数（SSIM）都得到了改善,更好并且很好地保留CT图像的细节信息。      

基于非抽样小波和边缘保持的自适应图像降噪

提出了一种基于非抽样小波和边缘保持的自适应图像降噪方法.利用小波系数的层间相关性理论及小波域系数模型理论,对小波变换得到的系数进行了分类和处理.与当前许多方法的做法不同,本文算法将图像的小波系数分成了与边缘相关的系数、与同性区域相关的系数和与噪声相关的系数,针对这3类系数的特点,使用不同的策略进行分别处理,保证了降噪的性能.实验结果表明,与传统方法相比,该方法不仅可以获得较清楚的图像边缘,而且降噪后图像质量优良.     

基于图像局部曲面可展化的混合噪声抑制算法

为满足基于旋翼无人机(UAV)载具的室外目标检测所需的低资源开销混合噪声抑制,该文提出一种基于图像局部曲面可展化的混合噪声抑制算法(DLS),该算法实现了局部曲面可展化算法和分层降噪算法优势互补,达到了两算法各自无法企及的降噪效果。首先,对图像进行局部可展化处理,抑制图像的椒盐噪声和低密度高斯噪声,得到初步降噪图像;接着,在空间域和傅里叶域分层降噪,在去除高斯噪声残余的同时,最大限度地保留图像边缘、纹理等细节;最后,迭代局部曲面可展化和分层降噪,进一步去除混合噪声残余成分,达到抑制目标检测图像混合噪声的目的。实验结果表明,在去除图像混合噪声时,相比于其他7种降噪算法,本文算法具有一定的优势,其降噪图像的主观视觉指标和客观数据指标统计优于其他7种算法。     

小波分析在图像降噪中的应用

本文在对图像降噪进行总体概述的基础上,介绍了传统降噪和小波降噪的原理,提出一种以阈值降噪法为基础的混合算法。然后用MATLAB中的小波工具箱对一个含有噪声图像进行降噪。通过实验结果的对比,可以看出新算法可以更为有效地降低噪声,并较好地保留图像的细节。     

基于图像局部曲面可展化的混合噪声抑制算法

为满足基于旋翼无人机(UAV)载具的室外目标检测所需的低资源开销混合噪声抑制,该文提出一种基于图像局部曲面可展化的混合噪声抑制算法(DLS),该算法实现了局部曲面可展化算法和分层降噪算法优势互补,达到了两算法各自无法企及的降噪效果.首先,对图像进行局部可展化处理,抑制图像的椒盐噪声和低密度高斯噪声,得到初步降噪图像;接着,在空间域和傅里叶域分层降噪,在去除高斯噪声残余的同时,最大限度地保留图像边缘、纹理等细节;最后,迭代局部曲面可展化和分层降噪,进一步去除混合噪声残余成分,达到抑制目标检测图像混合噪声的目的.实验结果表明,在去除图像混合噪声时,相比于其他7种降噪算法,本文算法具有一定的优势,其降噪图像的主观视觉指标和客观数据指标统计优于其他7种算法.     

Combining self-learning based super-resolution with denoising for noisy images

In this paper, we propose a new learning based joint Super-Resolution (SR) and denoising algorithm for noisy images. The individual processing of denoising and SR when super-resolving a noisy image has drawbacks such as noise amplification, blurring and SR performance reduction. In the proposed joint method, principal component analysis (PCA) based denoising is closely combined with a self-learning SR framework in order to minimize the SR visual quality degradation caused by noise. Experimental results show that the joint method achieves an SR image quality improvement in terms of noise and blurring, when compared with the state-of-the-art joint method and sequential combinations of individual denoising and SR.     

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.     

红外焦平面阵列非均匀性自适应校正方法

分析基于场景的红外焦平面阵列非均匀性校正方法中的景物退化和鬼影现象,提出了一种基于边缘约束高斯滤波的红外焦平面阵列非均匀性自适应校正方法。该方法设计了一个边缘约束高斯滤波器来获取理想的估计图像,利用最陡下降法得到计算增益校正因子和偏移量校正因子的迭代公式,并通过迭代步长的自适应控制来增快算法的收敛速度。通过仿真实验和真实红外图像处理对比实验表明:相较于目前已有的方法,该方法在有效抑制景物退化和鬼影现象的同时,较好地去除原始红外图像的固定图案噪声,保留了图像细节信息,提高了图像质量。     

Image blind restoration based on degradation representation network

Most deep learning (DL)-based image restoration methods have exploited excellent performance by learning a non-linear mapping function from low quality images to high quality images. However, two major problems restrict the development of the image restoration methods. First, most existing methods based on fixed degradation suffer from significant performance drop when facing the unknown degradation, because of the huge gap between the fixed degradation and the unknown degradation. Second, the unknown-degradation estimation may lead to restoration task failure due to uncertain estimation errors. To handle the unknown degradation in the real application, we introduce a degradation representation network for single image blind restoration (DRN). Different from the methods of estimating pixel space, we use an encoder network to learn abstract representations for estimating different degradation kernels in the representation space. Furthermore, a degradation perception module with flexible adaptability to different degradation kernels is used to restore more structural details. In our experiments, we compare our DRN with several state-of-the-art methods for two image restoration tasks, including image super-resolution (SR) and image denoising. Quantitative results show that our degradation representation network is accurate and efficient for single image restoration.     

非凸性优化与动态自适应滤波的湍流退化视频复原

针对目标探测器在大气中高速飞行时受湍流干扰,导致光学系统接收到的视频/图像产生像素偏移、模糊、信噪比降低等问题,本文对湍流退化视频/图像复原的复杂性及复原方法进行了研究,提出了一种基于非凸势函数优化与动态自适应滤波的湍流退化视频复原方法。首先,研究了湍流退化视频的求和与去模糊框架,并通过利用非刚性配准方法对刚性全局配准方法进行改进,进一步缩小了模糊核的尺度;然后,在计算机视觉的非凸优化框架下,构建了图像解卷积的非凸性算法,有效地解决了图像解卷积难题;最后,结合湍流退化视频自身特点,对超分辨率视频复原的动态自适应滤波框架进行了扩展与改进,使其适用于湍流退化视频的复原。仿真实验结果表明,本文方法的复原效果不仅有较大提升,而且实现了对湍流退化视频序列的动态自适应复原。     

Learning-based image restoration for compressed images

In this paper, we propose a novel learning-based image restoration scheme for compressed images by suppressing compression artifacts and recovering high frequency (HF) components based upon the priors learnt from a training set of natural images. The JPEG compression process is simulated by a degradation model, represented by the signal attenuation and the Gaussian noise addition process. Based on the degradation model, the input image is locally filtered to remove Gaussian noise. Subsequently, the learning-based restoration algorithm reproduces the HF component to handle the attenuation process. Specifically, a Markov-chain based mapping strategy is employed to generate the HF primitives based on the learnt codebook. Finally, a quantization constraint algorithm regularizes the reconstructed image coefficients within a reasonable range, to prevent possible over-smoothing and thus ameliorate the image quality. Experimental results have demonstrated that the proposed scheme can reproduce higher quality images in terms of both objective and subjective quality.     

Noise reduction using multiscale bilateral decomposition for digital color images

In this paper, we propose a noise reduction algorithm for digital color images using a nonlinear image decomposition approach. Most existing noise reduction methods do not adequately consider spatial correlation of color noise in digital color images. Color noise components in color images captured by digital cameras are observed as irregular grains with various sizes and shapes, which are spatially randomly distributed. We use a modified multiscale bilateral decomposition to effectively separate signal and mixed-type noise components, in which a noisy input image is decomposed into a base layer and several detail layers. A base layer contains strong edges, and most of noise components are contained in detail layers. Noise components in detail layers are reduced by an adaptive thresholding function. We obtain a denoised image by combining a base layer and noise-reduced detail layers. Experimental results show the effectiveness of the proposed algorithm, in terms of both the peak signal-to-noise ratio and visual quality.     

Dictionary learning and similarity regularization based image noise reduction

Redundant dictionary learning based image noise reduction methods explore the sparse prior of patches and have proved to lead to state-of-the-art results; however, they do not explore the non-local similarity of image patches. In this paper we exploit both the structural similarities and sparse prior of image patches and propose a new dictionary learning and similarity regularization based image noise reduction method. By formulating the image noise reduction as a multiple variables optimization problem, we alternately optimize the variables to obtain the denoised image. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual results and some numerical guidelines.     

Image quality assessment based on a degradation model

We model a degraded image as an original image that has been subject to linear frequency distortion and additive noise injection. Since the psychovisual effects of frequency distortion and noise injection are independent, we decouple these two sources of degradation and measure their effect on the human visual system. We develop a distortion measure (DM) of the effect of frequency distortion, and a noise quality measure (NQM) of the effect of additive noise. The NQM, which is based on Peli's (1990) contrast pyramid, takes into account the following: 1) variation in contrast sensitivity with distance, image dimensions, and spatial frequency; 2) variation in the local luminance mean; 3) contrast interaction between spatial frequencies; 4) contrast masking effects. For additive noise, we demonstrate that the nonlinear NQM is a better measure of visual quality than peak signal-to noise ratio (PSNR) and linear quality measures. We compute the DM in three steps. First, we find the frequency distortion in the degraded image. Second, we compute the deviation of this frequency distortion from an allpass response of unity gain (no distortion). Finally, we weight the deviation by a model of the frequency response of the human visual system and integrate over the visible frequencies. We demonstrate how to decouple distortion and additive noise degradation in a practical image restoration system     

Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior.

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.