基于跨尺度低秩约束的单幅图像盲超分辨率算法

单幅图像盲超分辨率方法是在模糊核未知的情况下仅利用单幅低分辨率图像重建高分辨率图像,这是一个严重的欠定逆问题.超分辨率正则化方法通过正则化约束项引入附加信息,为低分辨率图像恢复或重建合理的高频成分.本文将跨尺度自相似性与低秩先验相结合,提出了一种基于跨尺度低秩约束的单幅图像盲超分辨率方法,采用联合建模的方法同时估计模糊核与高分辨率图像.利用高分辨率图像、低分辨率图像及其降采样图像之间的跨尺度自相似性,对于低分辨率图像中的图像块在降采样图像中搜索相似块,将该图像块在高分辨率重建图像中对应的父块与其相似块在低分辨率图像中对应的父块合并,构造跨尺度相似图像块组矩阵.由于低分辨率图像中的跨尺度相似图像块能够为重建图像块提供潜在的细节信息,因此对相似图像块组矩阵进行低秩约束,在迭代求解过程中迫使重建图像恢复高频成分,进而促使模糊核的估计更加准确.此外,低秩约束能够表示数据的全局结构,对噪声具有鲁棒性.在真实和模拟图像上的实验表明,本文的算法能够准确地估计模糊核,重建高分辨率图像的边缘和细节,优于现有的自监督盲超分辨率算法.     

超分辨率重建技术研究进展

超分辨率(Super Resolution,SR)重建技术是指由一些低分辨率(Low Resolution,LR)模糊的图像或视频序列来估计具有更高分辨率(High Resolution,HR)的图像或视频序列,同时能够消除噪声以及由有限检验器尺寸和光学元件产生的模糊,是提高降质图像或序列分辨率的有效手段。首先介绍超分辨率重建所基于的成像系统模型,并对现有的图像超分辨率重建算法进行总结,重点对基于学习的超分辨重建算法进行对比分析,最后指出超分辨率重建技术的发展方向。     

L1范数的图像超分辨率重建改进算法

介绍了超分辨率图像重建的数学模型和基于L1范数的超分辨率重建算法。针对在所观察到的低分辨率图像不足情况下的超分辨率重建,在L1范数重建算法框架下,提出了一种新的代价方程,在其中增加了关于丢失的低分辨率观察信息的保真度项和正则化项。该方法同时对高分辨率图像和丢失的观察信息进行迭代估计,并利用交替最小方法求解。实验结果表明,在获取低分辨率图像较少的情况下,提出的算法能够有效地改进重建的结果。     

基于降质信息估计的盲图像复原算法

针对低质量图像的复原重建问题,提出了一种基于降质信息估计的盲图像复原算法.该算法主要包括噪声估计网络、模糊核估计网络和重建网络3部分.首先分别通过噪声估计网络和模糊核估计网络,对图像噪声水平和模糊核进行估计;其次,将估计所得噪声水平和模糊核作为降质信息,并联合待处理的低质量图像一起输入重建网络,以帮助获得更好的重建效果...     

一种有效的图像复原方法

图像超分辨率复原技术是由一序列低分辨率变形图像估计一幅或多幅较高分辨率的非变形图像,同时还能够消除加性噪声以及由有限检测器尺寸和光学产生的模糊,是图像融合领域中的一个重要分支。先把高分辨率图像变换成低分辨率图像,然后对低分辨率图像进行运动估计和运动补偿计算,最后再把低分辨率图像映射灰高分辨率图像。     

压缩视频超分辨率重建技术

超分辨率图像重建就是由低分辨率图像序列来估计高分辨率图像,而压缩视频的重建正成为当前研究的热点。本文首先分析了压缩视频重建的基础,建立了高、低分辨率图像间的关系,给出了量化噪声和运动矢量的模型;接着对目前最具有代表性的最大后验概率(MAP)算法、凸集投影(POCS)算法和迭代反向投影(IBP)算法进行了详细的阐述,并分别给出了实验结果;然后分析了运算的复杂度,介绍了几种实时化方法;最后针对目前存在的问题进行了展望,指出降质模型、运动估计、重建算法和实时应用将是今后研究的重点。     

基于自训练字典学习的单幅图像的超分辨率重建

针对单幅低分辨率图像的超分辨率重建问题,提出了一种基于自训练字典学习的超分辨率重建算法。首先根据图像的退化模型,对输入的低分辨率图像进行降质处理,然后利用 K-SVD 方法训练字典,获得重建所需要的先验知识,最后根据先验知识重建高分辨率图像。仿真实验的结果表明,利用该方法获得的高分辨率图像在视觉效果和客观评价上均优于传统方法,同时算法的时间效率也有很大的提升。     

一种改进的POCS算法的超分辨率图像重建

图像超分辨率是指从一组模糊的低分辨率图像重建一帧清晰的高分辨率图像的过程.从经典的基于凸集投影POCS(projection onto convex set)的超分辨率图像重建算法出发,分析重建后高分辨率图像边缘模糊的成因,提出了一种基于保留边缘信息的POCS超分辨率图像重建算法.实验结果表明该方法能够明显地提高重建图像的质量.     

稀疏条件下基于散射点估计的SAR切片超分辨重建

从合成孔径雷达(SAR)成像模型出发,在稀疏条件下,该文结合散射中心理论,从低分辨率图像中估计高分辨率图像的散射点参数,用若干sinc函数对感兴趣目标区(ROI)进行重建并抑制旁瓣,获得超分辨ROI切片。基于非线性最小二乘(NLS)估计给出了该超分辨重建问题的迭代求解算法,并以TerraSAR-X数据进行仿真验证,仿真结果表明,该文所提方法相比双立方插值和1范数正则化方法能够获得更高的空间分辨率与目标杂波比(TCR)。后续分析表明,散射点参数的估计精度受到信噪比和sinc函数重建3 dB带宽共同影响,重建3 dB带宽越大对噪声的鲁棒性越强。     

多级跳线连接的深度残差网络超分辨率重建

由于快速的卷积神经网络超分辨率重建算法(FSRCNN)卷积层数少、相邻卷积层的特征信息之间缺乏关联性,因此难以提取到图像深层信息导致图像超分辨率重建效果不佳。针对此问题,该文提出多级跳线连接的深度残差网络超分辨率重建方法。首先,该方法设计了多级跳线连接的残差块,在多级跳线连接的残差块基础上构造了多级跳线连接的深度残差网络,解决相邻卷积层的特性信息缺乏关联性的问题;然后,使用随机梯度下降法(SGD)以可调节的学习率策略对多级跳线连接的深度残差网络进行训练,得到该网络超分辨率重建模型;最后,将低分辨率图像输入到多级跳线连接的深度残差网络超分辨率重建模型中,通过多级跳线连接的残差块得到预测的残差特征值,再将残差图像和低分辨率图像组合在一起转化为高分辨率图像。该文方法与bicubic, A+, SRCNN, FSRCNN和ESPCN算法在Set5和Set14测试集上进行了对比测试,在视觉效果和评价指标数值上该方法都优于其它对比算法。     

Video super resolution based on non-local regularization and reliable motion estimation

Video super-resolution (SR) is a process for reconstructing high-resolution (HR) images by utilizing complementary information among multiple low-resolution (LR) images. Accurate estimation of the motion among the LR images significantly affects the quality of the reconstructed HR image. In this paper, we analyze the possible reasons for the inaccuracy of motion estimation and then propose a multi-lateral filter to regularize the process of motion estimation. This filter can adaptively correct motion estimation according to the estimation reliability, image intensity discontinuity, and motion dissimilarity. Furthermore, we introduce a non-local prior to solve the ill-posed problem of HR image reconstruction. This prior can fully utilize the self-similarities existing in natural images to regularize the HR image reconstruction. Finally, we employ a Bayesian formulation to incorporate the two regularizations into one Maximum a Posteriori (MAP) estimation model, where the HR image and the motion estimation can be refined progressively in an alternative and iterative manner. In addition, an algorithm that estimates the blur kernel by analyzing edges in an image is also presented in this paper. Experimental results demonstrate that the proposed approaches are highly effective and compare favorably to state-of-the-art SR algorithms.     

A nonlinear least square technique for simultaneous image registration and super-resolution.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.     

Multiframe super-resolution based on half-quadratic prior with artifacts suppress

The multiframe super-resolution (SR) technique aims to obtain a high-resolution (HR) image by using a set of observed low-resolution (LR) images. In the reconstruction process, artifacts may be possibly produced due to the noise, especially in presence of stronger noise. In order to suppress artifacts while preserving discontinuities of images, in this paper a multiframe SR method is proposed by involving the reconstruction properties of the half-quadratic prior model together with the quadratic prior model using a convex combination. Moreover, by analyzing local features of the underlined HR image, these two prior models are combined by using an automatically calculated weight function, making both smooth and discontinuous pixels handled properly. A variational Bayesian inference (VBF) based algorithm is designed to efficiently and effectively seek the solution of the proposed method. With the VBF framework, motion parameters and hyper-parameters are all determined automatically, leading to an unsupervised SR method. The efficiency of the hybrid prior model is demonstrated theoretically and practically, which shows that our SR method can obtain better results from LR images even with stronger noise. Extensive experiments on several visual data have demonstrated the efficacy and superior performance of the proposed algorithm, which can not only preserve image details but also suppress artifacts.     

基于图像先验和结构特征的盲图像复原算法

提出了一种基于图像先验和图像结构特征的盲图像复原算法,在模糊核未知的情况下,采用一系列离散化的模糊核参数对模糊图像进行非盲去卷积,得到一系列对应的复原图像。同时提出一种复原图像判决准则,对这一系列复原图像进行质量判决,从中得到最优的复原图像。最后在实验部分,通过对图像的测试表明,提出的盲图像复原算法能较准确的得到最优复原图像,复原效果在主观和客观标准上均有良好表现。     

Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.     

Blind single-image super resolution based on compressive sensing

Blind super resolution is an interesting area in image processing that can restore high resolution (HR) image without requiring prior information of the volatile point spread function (PSF). In this paper, a novel framework is proposed for blind single-image super resolution (SISR) problem based on compressive sensing (CS) framework that is one of the first works that considers general PSFs. The fundamental idea in the proposed approach is to use sparsity on a known sparse transform domain as a powerful regularizer in both the image and blur domains. Therefore, a new cost function with respect to the unknown HR image patch and PSF kernel is presented and minimization is performed based on two subproblems that are modeled similar to that of CS. Simulation results demonstrate the effectiveness of the proposed algorithm that is competitive with methods that use multiple LR images to achieve a single HR image.     

Zernike-moment-based image super resolution

Multiframe super-resolution (SR) reconstruction aims to produce a high-resolution (HR) image using a set of low-resolution (LR) images. In the process of reconstruction, fuzzy registration usually plays a critical role. It mainly focuses on the correlation between pixels of the candidate and the reference images to reconstruct each pixel by averaging all its neighboring pixels. Therefore, the fuzzy-registration-based SR performs well and has been widely applied in practice. However, if some objects appear or disappear among LR images or different angle rotations exist among them, the correlation between corresponding pixels becomes weak. Thus, it will be difficult to use LR images effectively in the process of SR reconstruction. Moreover, if the LR images are noised, the reconstruction quality will be affected seriously. To address or at least reduce these problems, this paper presents a novel SR method based on the Zernike moment, to make the most of possible details in each LR image for high-quality SR reconstruction. Experimental results show that the proposed method outperforms existing methods in terms of robustness and visual effects.     

自适应聚类的全耦合稀疏学习算法

在图像处理领域,基于稀疏表示理论的图像超分辨力算法、高低分辨力字典与稀疏编码之间的映射关系是其中的2个关键环节。由于丰富多样的图像类型,单一字典并不能很好地表示图像。而在稀疏编码之间的映射关系上,严格相等的约束关系也限制了图像重建的效果。针对上述两个方面,采用包容性更强的多个字典与约束条件更为宽松的全耦合稀疏关系进行图像的超分辨力重建。在图像非局部自相似性的基础上,进行多次自适应聚类;挑选出最优的聚类,通过全耦合稀疏学习的图像超分辨力算法,得到多个字典;最后,对输入的低分辨力图像进行分类重建,得到高分辨力图片。实验结果表明,在图像Leaves,Barbara,Room上,本文的聚类算法比原全耦合稀疏学习算法在峰值信噪比(PSNR)上分别提升了0.51 dB,0.21 dB,0.15 dB。     

Superresolution video reconstruction with arbitrary samplinglattices and nonzero aperture time

Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.