基于多种先验结合的图像盲去模糊方法

针对传统图像盲去模糊方法中部分细节复原不准确的问题,提出了一种结合暗通道先验、梯度先验以及强度先验的图像盲去模糊方法。该方法利用清晰图像本身的稀疏性以及边缘信息的稀疏性,结合清晰图像暗通道的稀疏性,在不同权重下对清晰图像以及模糊核进行约束。通过交替迭代的方式更新潜影和模糊核,以估计出的最精细的模糊核结合非盲去模糊方法最终得到去模糊图像。实验结果表明,所提方法对图像去模糊质量有所提升,有效地恢复了图像的一些细节。     

基于散斑图像梯度域的数字图像相关盲去模糊方法EI北大核心CSCD

针对复杂测量环境或高动态测量过程中出现的运动模糊问题,提出了一种灰度稀疏先验与参考图像梯度域先验相结合的散斑图像盲去模糊方法。该方法以灰度直方图峰值的L 0范数与参考图像梯度域分布建立优化函数正则项,使用二次分裂方法估计清晰图像,再以交替迭代的方式进行卷积核细化。在模糊核估计完成后,使用Richardson-Lucy非盲去卷积方法完成散斑图像的复原。实验结果证明:所提出的散斑图像盲去模糊方法与针对自然图像与文本图像的经典方法相比,获得了更优的图像去模糊效果,并提高了数字图像相关测量精度与鲁棒性。     

改进的局部最小像素先验遥感图像盲复原算法

为了解决遥感图像盲复原时模糊核估计不准确、复原图像存在振铃效应的问题,提出改进的局部最小像素先验遥感图像盲复原算法。该算法首先引入极端通道先验与局部最小像素先验结合,对图像的强度进行更好的约束,有利于得到更好的潜在清晰图像;然后采用基于梯度的方法估计模糊核,模糊核估计与中间潜在清晰图像估计交替迭代进行,获得较为理想的模糊核;最后引入联合双边滤波器,采用改进的拉普拉斯与正则化图像复原算法抑制图像复原的振铃效应。实验结果表明,本文方法对遥感图像复原效果较好,恢复的图像边缘清晰,振铃伪影得到抑制且模糊核较为理想;客观评价指标峰值信噪比(PSNR)较前沿复原算法平均提高约1.40 dB,结构相似度(SSIM)平均提高约0.02。     

基于L_(1/2)/L_2正则化的图像盲去模糊方法

图像去模糊是图像识别和视频分析的基础性工作.在实际应用中,多数情形是模糊核未知的盲去模糊问题.盲去模糊问题是一个病态问题,通常需建立某种正则化模型求解.已有的图像去模糊正则化模型难以恢复模糊图像的细节,本文提出了一种基于L1/2/L2的正则化模型,并设计了求解该模型的交替投影迭代算法.数值实验表明,所提出的模型和算法能够更好地恢复模糊图像的细微结构,并且计算效率高,对参数的鲁棒性强.     

基于颜色空间变换和CNN的自适应去模糊方法

目的研究数字图像中的去模糊问题,从受损的模糊图像中恢复出清晰图像。方法针对现有图像去模糊算法无法保留图像高频信息及容易产生振铃效应等问题,提出一种基于Y通道反卷积和卷积神经网络的两阶段自适应去模糊算法(SDYCNN)。在第1阶段,将数字图像转换至YUV颜色空间,根据图像无参考质量评价分数与模糊核尺寸之间的对应关系,在Y通道内自适应确定模糊核尺寸并进行反卷积增强;第2阶段将第1阶段中的反卷积增强作为预处理方式,通过4层卷积神经网络建立反卷积增强后的图像与清晰图像之间的映射关系,实现图像去模糊。结果轻微模糊图像在第1阶段便能够得到较好的去模糊效果,严重模糊图像经过第1阶段的反卷积增强,也有助于神经网络中特征的快速提取。结论实验结果表明,该算法不仅对于模糊图像具有良好的恢复效果,运算效率也有显著提升。     

基于强度和梯度先验的L0正则化模糊QR码识别

目的研究因机械抖动,拍摄器材与图像存在一定距离或相对运动而产生运动模糊、散焦模糊等情况下的模糊QR码图像识别。方法采用基于强度和梯度先验的L_0正则化方法对模糊QR图像进行去模糊。优化模糊核尺寸的人为预估问题,提高程序效率。对1至15类常用QR码图像进行模糊仿真,再通过盲提取获得模糊核,用峰值信噪比PSNR值衡量该方法在QR码图像去模糊的复原精度。结果PSNR值随着QR码图像复杂度的增加而相对减少,但因QR码存在一定的容错率,在PSNR值为13以上且噪声、振铃小的情况下就能够被识别。文中算法相较于其他算法在型号较高的模糊QR码恢复方面识别率更高。结论基于强度和梯度先验的L0正则化方法对模糊QR码的恢复效果显著,且不是只针对某一类模糊QR码图像,对于多种类型的模糊QR码图像恢复都能有很好的效果。     

贝叶斯推理模型耦合非平稳边缘保持先验的图像模糊消除

目的针对现行图像去模糊消除机制忽略了图像空间结构特征,降低了模糊消除效果,且算法稳定性不佳,无法克服解模糊等的不足,提出了贝叶斯模型耦合非平稳先验的图像去模糊机制。方法基于二阶统计量方法,定义模糊函数;引入滤波因子和超参数,构造非平稳边缘保持先验模型;基于贝叶斯推理,引入雅克比矩阵设计了超参数动态更新机制;用耦合先验模型与贝叶斯模型完成图像复原。在仿真平台上测试了算法的性能。结果与其他几种机制相比,提出的算法机制去模糊质量更好,局部放大后纹理细节仍然清晰,并且去模糊前后图像的结构相似度更高。结论提出的算法具有较佳的图像去模糊效果,重构质量理想。     

基于生成对抗网络的QR码去模糊算法

目的 针对包装产品上QR码在采集过程中的运动模糊、失焦模糊,长期磨损形成的自模糊和环境中的噪声等因素,导致QR码无法识别的问题,提出一种基于生成对抗网络的QR码去模糊算法。方法 采用深度学习模型生成对抗网络对模糊核和环境噪声具有的强大拟合和估计能力,提取模糊QR码图像与真实图像的深层特征和差距,并通过生成器与判别器不断迭代对抗,使生成器具有由输入的模糊QR码产生与之对应的去模糊QR码图像的能力。结果 生成器能较好地对模糊核和环境噪声进行估计,而且能够实现对数据集内多种不同模糊程度QR码的去模糊,去模糊QR码图像效果较好,处理时间快,识别率较高。结论 采用基于生成对抗网络的QR码去模糊算法能够广泛应用于包装产品外壳上QR码的预处理过程,泛化能力较好,能有效提高扫描识别率。     

正则化恢复联合稀疏表示的图像超分辨率重构

为改善强降质图像的分辨率水平,提出了一种正则化恢复联合稀疏表示的单帧图像超分辨率重构框架。为同时放大图像并抑制模糊及噪声,首先根据退化估计正则化平衡极小问题的逼近项和先验项,然后基于初步的锐利清晰图像和预先建立的图像超完备稀疏表示字典实现边缘保持的图像分辨率放大。正则化恢复的输出改善了传统学习法图像超分辨中低频分量的双立方插值版本,同时对降质的有效抑制降低了字典原子对退化信息的依赖性。实验结果表明,本方法可对模糊含噪的低分辨率图像实现有效的超分辨率重构。     

基于倒频谱的运动模糊图像恢复

图像去模糊是图像处理中的基本问题,在成像系统中,引起图像退化的原因有很多。例如噪声的影响,就是引起图像降质的主要原因之一,但另一个主要原因就是在成像系统的散焦,成像设备与物体的相对运动,成像器材的固有缺陷或外部干扰等过程中成像产生模糊。主要针对图像恢复中的模糊情况,利用倒频谱把模糊信息和原图像信息分离出来,从而可以估计出模糊图像的模糊尺度。并对估计值进行仿真实验,取得较好的效果。     

Single-shot blind uniform motion deblurring with ringing reduction

In this paper, we have proposed a blind motion deblurring algorithm that comprises the estimation of the motion blur parameters (length and angle) in a modified cepstrum domain with a blind no-reference image spatial quality evaluator (BRISQUE) used for the tuning of point spread function (PSF) parameters. Ringing artifacts are generated during the deblurring process. In this paper, the modified R–L (Richardson–Lucy) algorithm with weight calculation based on graphcut is presented to obtain good estimates of the unblurred image with ringing reduction. The method involves the selection of different weights for edges and smooth regions such that the ringing effect over R–L iterations can be reduced. A newly proposed method has been tested on various natural images with a motion blur of different length and degrees. A comparison with state-of-the-art methods proves that the proposed technique achieved better results in terms of different quality measures such as SSIM, FSIM and PSNR and can be greatly beneficial for deblurring purpose.     

Wavelet Based Restoration of Images with Missing or Damaged Pixels

This paper addresses the problem of how to restore degraded images where the pixels have been partly lost during transmission or damaged by impulsive noise. A wide range of image restoration tasks is covered in the mathematical model considered in this paper – e.g. image deblurring, image inpainting and super-resolution imaging. Based on the assumption that natural images are likely to have a sparse representation in a wavelet tight frame domain, we propose a regularization-based approach to recover degraded images, by enforcing the analysis-based sparsity prior of images in a tight frame domain. The resulting minimization problem can be solved efficiently by the split Bregman method. Numerical experiments on various image restoration tasks – simultaneously image deblurring and inpainting, super-resolution imaging and image deblurring under impulsive noise – demonstrated the effectiveness of our proposed algorithm. It proved robust to mis-detection errors of missing or damaged pixels, and compared favorably to existing algorithms.     

Defocus deblurring for catadioptric omnidirectional imaging based on spatially invariant point spread function

The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are applied. In order to overcome this problem, a novel method based on computational photography is proposed. Firstly, the defocus blur of catadioptric omnidirectional imaging is analyzed to calculate the point spread function for different scene points. Then, the defocus blur kernel of omnidirectional image is confirmed to be spatially invariant when rotating the focus ring of camera lens during an image’s integration time. Lastly, the deconvolution algorithm using prior sparse derivatives is applied to obtain all-focused/sharp omnidirectional images. Experimental results demonstrate that the proposed method is effective for omnidirectional image deblurring and can be applied to most existing catadioptric omnidirectional imaging systems.     

A novel blind super‐resolution technique based on the improved Poisson maximum a posteriori algorithm

Image restoration has received considerable attention. In many practical situations, unfortunately, the blur is often unknown, and little information is available about the true image. Therefore, the true image is identified directly from the corrupted image by using partial or no information about the blurring process and the true image. In addition, noise will be amplified to induce severely ringing artifacts in the process of restoration. This article proposes a novel technique for the blind super‐resolution, whose mechanism alternates between de‐convolution of the image and the point spread function based on the improved Poisson maximum a posteriori super‐resolution algorithm. This improved Poisson MAP super‐resolution algorithm incorporates the functional form of a Wiener filter into the Poisson MAP algorithm operating on the edge image further to reduce noise effects and speed restoration. Compared with that based on the Poisson MAP, the novel blind super‐resolution technique presents experimental results from 1‐D signals and 2‐D images corrupted by Gaussian point spread functions and additive noise with significant improvements in quality. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 12, 239–246, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10032     

Noise robust one-dimensional blind deconvolution of medical ultrasound images

Recently, several blind cepstral deconvolution methods for medical ultrasound images were compared experimentally. The results indicated that the generalized cepstrum or the complex cepstrum with phase unwrapping give the blind homomorphic deconvolution algorithms with the best performance. However, the frequency domain phase unwrapping for pulse estimation, which is an essential part of both methods, is sensitive to the sensor noise when the values of the spectrum are small due to the randomness of the tissue response. The noise introduces abrupt changes in the phase. The phase degradation due to the noise causes variable spatial and gray scale resolution in image sequences following deconvolution. This paper introduces a noise robust Bayesian phase unwrapping method using a noncausal Markov random chain model. The prior regularizing term accounts for the noise and smoothes the phase. The phase unwrapping is formulated as a least mean square optimization problem. The optimization is done noniteratively by solving a difference equation using the cosine transform. The resulting improvement in radial and lateral blind deconvolution is demonstrated on six short ultrasound image sequences recorded in vitro or in vivo.     

Iterative statistical approach to blind image deconvolution

Image deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum a posteriori approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation-maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our a priori knowledge available about the object and the blur.     

基于深度强化学习的图像去模糊方法

目的为了有效地去除多种图像模糊,提高图像质量,提出基于深度强化学习的图像去模糊方法。方法选用GoPro与DIV2K这2个数据集进行实验,以峰值信噪比(PSNR)和结构相似性(SSIM)为客观评价指标。通过卷积神经网络获得模糊图像的高维特征,利用深度强化学习结合多种CNN去模糊工具建立去模糊框架,将峰值信噪比(PSNR)作为训练奖励评价函数,来选择最优修复策略,逐步对模糊图像进行修复。结果通过训练与测试,与现有的主流算法相比,文中方法有着更好的主观视觉效果,且PSNR值与SSIM值都有更好的表现。结论实验结果表明,文中方法能有效地解决图像的高斯模糊和运动模糊等问题,并取得了良好的视觉效果,在图像去模糊领域具有一定的参考价值。