拉普拉斯与双边滤波的图像去运动模糊算法

运动模糊图像盲复原是图像复原中的关键问题之一,由于模糊信息的复杂性以及图像噪声的影响,导致复原的结果有振铃效应.为解决这些问题,提出基于拉普拉斯先验的改进算法.在原有的模糊核估计过程中,引入拉普拉斯锐化增强,增强图像的边缘信息,达到改善模糊核估计结果的目的.在复原时,针对图像振铃噪声,引入了双边滤波,抑制该噪声对复原结果的影响,进一步改善复原图像的质量.与三种现有图像去模糊算法相比,该方法能更准确地估计模糊信息,对图像复原具有更好的鲁棒性.     

处理异常值的相机抖动模糊图像复原

摘 要：目的：在曝光过程中由于相机抖动而导致的图像模糊,是一种常见的图像降质现象,并且模糊图像中存在的异常值会导致复原结果的振铃效应,为了解决这些问题,本文提出一种处理异常值的相机抖动模糊图像复原算法。方法：该算法以自然图像统计为先验模型,结合变分贝叶斯方法和KL散度构造易于优化的代价函数,进而求出模糊核。针对异常值造成的振铃效应,在解卷积的过程中采用期望-最大值算法估计并处理异常值以抑制振铃效应。结果：采用本文方法对大量模糊图片进行复原,实验结果表明该方法能有效地去除相机抖动产生的模糊,在保持图像边缘和细节的同时,可有效抑制振铃效应。结论：本文提出了一种通过处理异常值达到抑制振铃效应目的,进而提高复原效果的图像盲复原新方法。实验结果表明该方法切实有效,并且该方法引出了一种抑制振铃效应的新思路。     

频域图像增强混合滤波算法及在作物缺素中的应用

针对目前常用的频域图像增强方法存在振铃现象、模糊效应与过度锐化,为突出图像的细节及边缘信息,保护图像原有的光滑区域灰度,提出一种新方法.利用低通和高通滤波器它们各自的优势,有选择性地对图像进行滤波处理,即对图像的高频和低频部分分别进行不同的系数增强.实验结果表明,该算法保持了图像的光滑区域灰度,改善了边缘区域的对比度,验证了该算法的有效性.针对作物缺素问题,运用不同滤波算法对采样图像进行处理后,对病斑部位采用相同的形状特征提取方法进行实验,实验结果表明,该算法突出了叶面纹络与褐色斑块,保持了样本图像的光滑区域灰度,有效改善了对病斑部位形状特征参数的提取效果,具有一定的可应用性.     

基于小波域降采样的低比特率图像压缩算法

针对无线传输背景下低比特率图像压缩存在的模糊和振铃问题,提出一种基于小波域降采样和插值的高效图像压缩算法.在编码端,对图像进行小波变换,根据各高频子带特点对其进行自适应降采样处理,并采用基于小波树的小波差分缩减编码算法进行编码.在解码端,采用基于边缘方向的插值方法将解码的高频子带进行放大,对解码重建的图像采用基于强边缘的自适应滤波算法以消除振铃效应.实验结果表明,与基于空域降采样的图像压缩算法相比,该算法具有较高的图像解码质量,可有效抑制低比特率下重建图像的模糊和振铃问题.     

盲复原图像振铃效应评价

线性空不变盲复原算法通常会在较为明显的边缘处产生振铃效应。振铃现象主要受噪声,复原算法种类,以及复原算法参数的选择的影响。提出了一种盲复原图像振铃效应评价方法。方法根据振铃效应的不同类型,使用Gabor滤波器,共生向量等方法分别对其进行评价,最后提出整体的振铃效应评价方法。实验结果表明,该方法可以有效地评价不同复原算法和不同复原参数下的复原图像中的振铃效应,评价结果符合主观评价结果。     

图像反卷积边界效应的快速抑制算法

基于频域的图像反卷积算法假设输入信号被周期延拓,但边界处的不连续会导致结果产生严重的振铃效应.为此,提出一种快速有效的边界效应抑制算法.首先在现有基于图像延拓的算法的基础上,将延拓区域的定义简化为3种类型;然后提出一种具有对称系数的卷积金字塔滤波器组模型.基于这种模型,在某种特殊图像上针对每种类型的区域各训练一组滤波器组系数,并将由此训练得到的滤波器组用于求解其他图像相应类型的延拓区域.实验结果表明,该算法避免了求解大型稀疏线性方程组,在不影响图像反卷积精度的前提下,可将延拓区域的计算速度提高2个数量级以上,有效地抑制各种频域反卷积算法的振铃效应.     

一种盲复原图像振铃效应的后处理与质量评价方法

由于图像盲复原过程中先验信息的不足,往往造成复原图像质量不尽如人意,振铃效应就是影响复原图像质量的重要因素之一。振铃效应的存在使得对复原图像的后续处理难于进行,并使得一些图像质量的评价方法失效。针对图像盲复原的特点,提出了一种振铃效应的后处理方法,并基于将振铃与清晰度分别评价的思想,提出了一种复原图像的无参考评价方案。实验证明该去振铃方法简单有效,该复原图像的评价方案也很好地反映了人类的视觉特性。     

基于Richardson-Lucy的图像去模糊新算法

针对传统的Richardson-Lucy(RL)算法存在振铃效应和噪声放大等问题,提出了一种基于RL的图像去模糊新算法,通过在迭代过程中引入增益图(GainMap),降低了复原图像的对比度,阻止迭代过程振铃效应的传播和噪声的进一步放大。详细讨论了引入的增益图的不同参数选择对去模糊结果的影响,通过选取合理的参数得到最佳的结果。实验结果验证了该算法抑制振铃效应的有效性,同时它很好地保留了图像细节,对含有噪声的模糊图像也有很好的复原效果。     

改进的互相关虹膜匹配算法

传统的相位互相关匹配算法对降质虹膜图像的识别效果并不理想,针对这一问题提出了一种改进的互相关虹膜匹配算法.该算法通过虹膜分割、归一化和图像增强对虹膜图像进行预处理.进一步研究了频域的高斯滤波器设计以提取虹膜特征的有效频率信息,去除高频噪声的影响.提出了一种新的虹膜匹配准则,这种准则有效地利用了全部的相位信息和部分幅值信息.该算法解决了传统算法中理想低通滤波器产生的振铃效应和仅仅使用图像相位信息的问题.实验结果表明,提出的算法具有更好的识别效果.     

基于非均匀模糊核的RL改进算法

针对传统均匀模糊模型下的Richardson Lucy（RL）算法存在振铃效应和细节丢失等问题,提出了一种基于非均匀模糊模型下的改进RL算法。基于Yu-Wing的射影运动模糊模型,通过在迭代过程中采用基于局部极值分解的方法提取含有图像结构但无振铃的边缘图像,将模糊图像和上述提取的边缘图像作为输入图像,用添加了局部先验项的RL法对输入图像进行去模糊。实验验证了算法抑制振铃效应的有效性,同时很好地保留了图像细节,表明非均匀模糊模型在相机抖动产生的模糊图像去模糊中更为有效。     

Image sharpening detection based on multiresolution overshoot artifact analysis

With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA). By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e., weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state-of-the-art approaches.

     

Reducing blocking artifacts in JPEG-compressed images using an adaptive neural network-based algorithm

It is well known that at low bit rates, a block-based discrete cosine transform compressed image or video can exhibit visually annoying blocking and ringing artifacts. Low-pass filters are very effective in reducing the blocking artifacts in smooth areas. However, it is difficult to achieve a satisfactory result for ringing artifact removal using only an adaptive filtering scheme. This paper presents a neural network-based deblocking method that is effective on various types of images. The first step of this scheme is block classification that identifies each 8 × 8 block as one of the three types: PLAIN, EDGE or TEXTURE, based on its statistical characteristics. The next step is the reduction in the blocking and ringing artifacts by applying three trained layered neural networks to three different types of image areas. Comparing this method with other algorithms, the simulation results clearly show that the proposed algorithm is very powerful in effectively reducing both blocking and ringing artifacts while preserving the true edge and textural information and thus significantly improving the visual quality of the blocking images or videos.     

An Edge-Preserving Image Reconstruction Using Neural Network

This paper presents an image restoration model based on the implicit function theorem and edge-preserving regularization. We then apply the model on the subband-coded images using the artificial neural network. The edge information is extracted from the source image as a priori nowledge to recover the details and reduce the ringing artifact of the subband-coded image. The multilayer perceptron model is employed to implement the restoration process. The main merit of the presented approach is that the neural network model is massively parallel with strong robustness for the transmission noise and parameter or structure perturbation, and it can be realized by VLSI technologies for real-time applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experimental results showed that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.     

A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter

Motion blur is a common problem in digital photography. In the dim light, a long exposure time is needed to acquire a satisfactory photograph, and if the camera shakes during exposure, a motion blur is captured. Image deblurring has become a crucial image-processing challenge, because of the increased popularity of handheld cameras. Traditional motion deblurring methods assume that the blur degradation is shift-invariant; therefore, the deblurring problem can be reduced to a deconvolution problem. Edge-specific motion deblurring sharpened the strong edges of the image and then used them to estimate the blur kernel. However, this also enhanced noise and narrow edges, which cause ambiguity and ringing artifacts. We propose a hybrid-based single image motion deblurring algorithm to solve these problems. First, we separated the blurred image into strong edge parts and smooth parts. We applied the improved patch-based sharpening method to enhance the strong edge for kernel estimation, but for the smooth part, we used the bilateral filter to remove the narrow edge and the noise for avoiding the generation of ringing artifacts. Experimental results show that the proposed method is efficient at deblurring for a variety of images and can produce images of a quality comparable to other state-of-the-art techniques.     

基于并行运算的双层图像锐化方法

针对低清晰度照片或图像放大后边界模糊、画质差及人们对高清图像的实际需求,基于统一计算架构(CUDA)环境,提出了一个两层结构的图像并行锐化方法,设计并实现了一个基于GPU的并行锐化算法：第一层采用并行线性插值法,反复对图像非边界部分进行计算以及边缘区域锐化处理;第二层采用改进的梯度法对图像进一步优化。放大后的图像经该方法处理后,基本上可消除图像边缘区域的锯齿,使图像画质平滑、自然、清晰。经实验验证,设计的基于GPU的并行锐化算法在效率和画质上都优于目前流行的算法,提出的方法可应用于现有图像及照片放大后处理。     

2维加窗全相位图像滤波模板的设计

为克服常用滤波模板的缺陷,消除传统图像滤波中存在的振铃现象和方块效应,提出了任意正交变换下的2维加窗全相位滤波算法.在严格证明任意正交变换下的2维全相位模板的计算通式基础上,进一步推导出傅里叶变换下的加窗全相位模板的简化公式.通过对亚奈图像的恢复实验证明：相比于反傅氏变换法生成的模板,用全相位DFT（discrete Fourier transform）模板恢复出的图像PSNR（峰值信噪比）值要高出7dB以上,且消除了振铃现象.通过实验还证明,用全相位DCT（discrete cosine transform）模板滤波可完全消除传统分块滤波中的方块效应.     

基于后验概率的图像模糊检测方法

数字图像被动盲取证是指在不依赖任何预签名提取或预嵌入信息的前提下,对图像的真伪和来源进行鉴别和取证。图像在经篡改操作时,为了消除图像在拼接边缘产生的畸变,篡改者通常会采用后处理消除伪造痕迹,其中,模糊操作是最常用的手法之一。提出一种人工模糊痕迹检测方法。将经过模糊操作后图像像素之间存在的高度相关性进行模型化表示;采用EM算法估算出图像中每个像素属于上述模型的后验概率;根据所得后验概率的大小进行模糊操作检测。实验结果表明,该算法能够有效地检测出篡改图像中的人工模糊痕迹,并对不同模糊类型、有损JPEG压缩以及全局缩放操作均具有较好的鲁棒性。     

Fast parallel grid warping-based image sharpening method

The development of image sharpening algorithms is currently one of the most important problems in image processing. This paper presents a parallel implementation of a pixel grid warping algorithm for image sharpening. To sharpen image edges, the proposed algorithm moves pixels in the edge neighborhood towards edge centers rather than changing pixel values directly. This approach does not increase noise and does not cause ringing artifacts.