基于稀疏表示的超分辨率图像重建

针对单幅低分辨率图像的超分辨率重建,提出一种基于稀疏表示的改进算法。通过联合输入低分辨率图像块和对应生成的高分辨率图像块,求解其在高低分辨率字典对上的稀疏表示系数,再将系数与高分辨率字典结合,修正输出的高分辨率图像块。仿真实验表明,文中提出的算法有效提升了重建图像的质量。     

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。     

嫦娥一号卫星CCD立体相机影像超分辨率重建算法

针对嫦娥一号卫星CCD立体相机空间分辨率不足的问题,运用最大后验概率估计法(MAP)实现了月表影像的超分辨率重建。介绍了嫦娥一号卫星CCD立体相机的成像模型,分析了图像获取过程中的主要影响因素,并建立了相应的超分辨率重建模型。基于该模型,首先采用误差-参数分析法估计嫦娥一号卫星CCD立体相机动态成像光学系统的点扩散函数(PSF);然后将估计的PSF应用到MAP算法所建立的目标函数中,采用共轭梯度法对目标函数进行最值求解;再通过VC软件平台编程实现了对单帧正视月表影像的超分辨率重建;最后从信息熵、清晰度和频谱等方面对重建图像进行评价,结果表明重建图像像质优良。     

基于稀疏表示的图像去噪和超分辨率重建

目前超分辨率的研究分成静态图像超分辨率和动态图像超分辨率两大类,静态图像超分辨率是指利用单张低分辨率图像内容来重建出高分辨率图像,本质上高分辨率图像的高频成分不能由原有低频成分算出,故如何补足高频成分以避免模糊现象是提升视觉质量的关键也是研究重点。图像去噪和超分辨率的目的是为了解决数字图像分辨率不足所提出的技术。这个技术主要是应用在某些只能得到单张低分辨率图像的场合,利用仅有的一张低分辨率图像来产生应用上所需的高分辨率图像。稀疏表示作为一种重要的数据编码与表达方式,不仅在人类的视觉认知机理上具有明确的理论依据,而且在信号表达与重建理论方面得到了严格的证明和推导。本文主要采用稀疏表示理论,对图像去噪和超分辨率重建的相关技术与算法进行研究。     

一种改进的超分辨率算法

利用多帧低分辨率图像重建一幅高分辨率图像成为迫切需要解决的难题,传统基于插值的超分辨率算法的发展受到了限制。本文基于重建方法,根据低分辨率图像帧间运动参数,提出了合理的权重分配算法。实验结果表明,图像超分辨率重建取得了良好效果。     

基于视频模式识别的快速超分辨率重建

针对传统超分辨率重建方法运算量过大的问题,提出一种快速超分辨率重建算法。该算法在快速最大后验概率（MAP）重建算法的基础上,针对车牌识别的特定应用进一步简化了代价函数。该算法从低分辨率的图像序列中提取出高分辨率的图像。这些高分辨率的图像不仅极大地提高了车牌的识别率,也降低了获取高分辨率图像所需成像系统硬件的要求。移动车辆视频的盲重建实验表明,该算法能够有效地提高车牌图像的质量,而其运算量要远远小于其他的超分辨率重建方法。较低的运算量使得该算法能够有效应用于实时系统。     

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.     

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.     

超分辨率重建算法综述

随着高分辨率移动设备和超高清电视的发展,对已有的低分辨率视频进行超分辨率上采样成为最近的一个研究热点.对已有的超分辨率重建算法根据输入输出方式的不同,分为多图像超分辨率重建、单图像超分辨率重建、视频超分辨率重建三大类,综述了其中每类算法的发展情况及常用算法,并对不同算法的特点分析比较.随后讨论了多图像超分辨率重建和单图像超分辨率重建方法对视频超分辨率重建方法的影响,最后展望了超分辨率重建算法的进一步发展.     

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。     

Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness

A new algorithm for single-image super-resolution based on selective sparse representation over a set of coupled dictionary pairs is proposed. Patch sharpness measure for high- and low-resolution patch pairs defined via the magnitude of the gradient operator is shown to be approximately invariant to the patch resolution. This measure is employed in the training stage for clustering the training patch pairs and in the reconstruction stage for model selection. For each cluster, a pair of low- and high-resolution dictionaries is learned. In the reconstruction stage, the sharpness measure of a low-resolution patch is used to select the cluster it belongs to. The sparse coding coefficients of the patch over the selected low-resolution cluster dictionary are calculated. The underlying high-resolution patch is reconstructed by multiplying the high-resolution cluster dictionary with the calculated coefficients. The performance of the proposed algorithm is tested over a set of natural images. PSNR and SSIM results show that the proposed algorithm is competitive with the state-of-the-art super-resolution algorithms. In particular, it significantly out-performs the state-of-the-art algorithms for images with sharp edges and corners. Visual comparison results also support the quantitative results.     

基于自适应字典稀疏表示的超分辨率算法

基于学习的超分辨率算法通过一组训练样例来学习一个字典,并从该字典中合成低分辨率图像中丢失的高频信息,最终得到相应的高分辨率图像。介绍了几种常用的基于学习的超分辨率算法,并提出了一种新的算法:基于自适应字典稀疏表示的超分辨率算法。实验结果表明,该方法在主观与客观上均具有较好的重建效果。     

基于改进K-SVD字典学习的超分辨率图像重构

 针对已有算法中字典训练的时间消耗巨大的问题,提出了一种改进的基于字典学习的超分辨率图像重构算法.本文将K-SVD字典算法和高低分辨率联合生成的思想结合起来,形成新的字典训练方法,并将由该算法生成的高低分辨率字典应用于基于稀疏表示的超分辨率重构.重构仿真实验证明算法不仅有效降低了字典训练所消耗的时间,而且能够改善重构高分辨图像的质量.     

Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding

Recently, single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries-based clustered sparse coding scheme for SISR. Firstly, a large number of high-resolution (HR) image patches are randomly extracted from a set of example training images and clustered into several groups of "geometric patches," from which the corresponding "geometric dictionaries" are learned to further sparsely code each local patch in a low-resolution image. A clustering aggregation is performed on the HR patches recovered by different dictionaries, followed by a subsequent patch aggregation to estimate the HR image. Considering that there are often many repetitive image structures in an image, we add a self-similarity constraint on the recovered image in patch aggregation to reveal new features and details. Finally, the HR residual image is estimated by the proposed recovery method and compensated to better preserve the subtle details of the images. Some experiments test the proposed method on natural images, and the results show that the proposed method outperforms its counterparts in both visual fidelity and numerical measures.     

Compression of facial images using the K-SVD algorithm

The use of sparse representations in signal and image processing is gradually increasing in the past several years. Obtaining an overcomplete dictionary from a set of signals allows us to represent them as a sparse linear combination of dictionary atoms. Pursuit algorithms are then used for signal decomposition. A recent work introduced the K-SVD algorithm, which is a novel method for training overcomplete dictionaries that lead to sparse signal representation. In this work we propose a new method for compressing facial images, based on the K-SVD algorithm. We train K-SVD dictionaries for predefined image patches, and compress each new image according to these dictionaries. The encoding is based on sparse coding of each image patch using the relevant trained dictionary, and the decoding is a simple reconstruction of the patches by linear combination of atoms. An essential pre-process stage for this method is an image alignment procedure, where several facial features are detected and geometrically warped into a canonical spatial location. We present this new method, analyze its results and compare it to several competing compression techniques.     

基于正则化稀疏表示的图像超分辨率算法

为了从单幅低分辨率(LR)图像恢复出高分辨率(H R)图像,提出了一种应用正则化稀疏表示和基于机器学习 的超分辨率(SR)图像恢复算法。构造了一种基于稀疏表示的SR凸变模型,为了提高 恢复效果,针对模型 提出了两种稀疏正则化约束条件,一是将分类效果更好的图表拉普拉斯作为正则化约束条件 ,从而找到与 输入LR图像块在结构上最接近的学习样本;另一种是针对冗余的学习样本进行约 束,保证了图像边 缘的锐利。将输入的每一块LR图像应用正则化稀疏表示,经过学习得到与之对应的HR图像块 , 最终得到整幅HR图像。试验结果表明,算法恢复出的HR图像峰值信噪比(PSNR )值较双三次插值算法最高提升约2dB,主观目视清晰、边缘锐利。     

Image super-resolution with sparse neighbor embedding

Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. In the first process, neighbor search is performed using the Euclidean distance metric, and in the second process, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. In this paper, we propose a sparse neighbor selection scheme for SR reconstruction. We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the k-nearest neighbor (k-NN) for reconstruction should have similar local geometric structures based on clustering, we employ a local statistical feature, namely histograms of oriented gradients (HoG) of low-resolution (LR) image patches, to perform such clustering. By conveying local structural information of HoG in the synthesis stage, the k-NN of each LR input patch is adaptively chosen from their associated subset, which significantly improves the speed of synthesizing the HR image while preserving the quality of reconstruction. Experimental results suggest that the proposed method can achieve competitive SR quality compared with other state-of-the-art baselines.     

基于图像块分类稀疏表示的超分辨率重构算法

 目前基于图像块稀疏表示的超分辨率重构算法对所有图像块都用同一字典表示,不能反映不同类型图像块间的差别.针对这一缺点,本文提出基于图像块分类稀疏表示的方法.该方法先利用图像局部特征将图像块分为平滑、边缘和不规则结构三种类型,其中边缘块细分为多个方向.然后利用稀疏表示方法对边缘和不规则结构块分别训练各自对应的低分辨率和高分辨率字典.重构时对平滑块利用简单双三次插值方法,边缘和不规则结构块由其对应的高、低分辨率字典通过正交匹配追踪算法重构.实验结果表明,与单字典稀疏表示算法相比,本文算法对图像边缘部分重构质量明显改善,同时重构速度显著提高.     

Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying sparsity, it is necessary to locally adapt the dictionary learning process and the sparsity-regularization parameters. However, spatial adaptation alone runs into the risk of over-fitting the data because variation and invariance are two sides of the same coin. In this work, we propose two sets of complementary ideas for regularizing image reconstruction process: (1) the sparsity regularization parameters are locally estimated for each coefficient and updated along with adaptive learning of PCA-based dictionaries; (2) a nonlocal self-similarity constraint is introduced into the overall cost functional to improve the robustness of the model. An efficient alternative minimization algorithm is present to solve the proposed objective function and then an effective image reconstruction algorithm is presented. The experimental results on image deblurring, super-resolution and compressive sensing demonstrate that the proposed image reconstruct method outperforms many existing image reconstruction methods in both PSNR and visual quality assessment.