图像超分辨率重建算法研究
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摘要
在实际应用中,由于成像系统内在硬件设备的限制,人们常常无法获得高分辨率的图像。通过改善硬件设备来提高图像的分辨率代价很高,而且对于特定成像系统短期内很难克服一些技术难题,所以从软件方面来提高图像的分辨率意义重大。图像的超分辨率重建就是指通过数字图像处理的方法从一幅或序列多幅低分辨率观测图像中重建出一幅高分辨率的图像。本文致力于解决当对场景只有一幅低分辨率观测图像的情况下,如何结合图像的一些先验知识,恢复出图像获取时丢失的高频信息,重建出一幅高分辨率的图像。
本文对基于插值的超分辨率算法进行研究。考虑到传统的基于插值的超分辨方法是建立在图像的连续性假设之上的,虽然实时性非常好,但是重构的高分辨率图像在边缘上的效果不好。本文提出了基于图像局部自相似性的插值算法,我们首先对低分辨率图像进行自适应四叉树分块,然后利用图像在局部区域上表现出的自相似性,对每个局部区域的插值参数进行最小二乘估计,并据此完成插值计算,最后运用小波域投影算子对插值得到的高分辨率图像进行全局优化得到最终的高分辨率图像。本文算法大大改善了传统插值算法在图像边缘上的振铃效应,而且运算效率很高。
本文对基于最大后验概率(Maximum a Posteriori,MAP)的概率分析超分辨率算法进行研究。基于MAP的概率分析超分辨率算法可以将高分辨率目标的一些先验知识以先验概率密度函数的形式加入到优化问题的正则约束项中,将超分辨率问题转化为随机正则优化问题来求解,有非常好的理论基础,因此基于MAP的的超分辨率算法一直是一个研究的热点。本文将小波域Implicit Markov Random Field (IMRF)模型结合到图像超分辨率算法中来,构造了新的优化目标函数,提出了基于小波域IMRF模型的图像超分辨率算法。本文算法可以很好的克服小波域Hidden Markov Tree (HMT)模型超分辨率算法计算量非常大的缺陷,而且较小波域HMT超分辨率算法能得到更好的超分辨率重建结果。
本文对基于学习的超分辨率算法进行研究。基于学习的超分辨率算法需要构造低分辨率和高分辨率图像样本库,通过学习样本库得到低分辨率图像和高分辨率图像的内在联系,从而指导图像超分辨率重建。这类算法需要构造样本库,而且最终超分辨率的效果好坏很大程度上依赖于待处理图像和样本库图像之间的相似性。本文利用Haar(?)、波变换细节子块之间相近的自相似性,并运用Bp神经网络方法来估计这种自相似关系,提出了一种结合Haar小波变换和Bp神经网络的自学习的图像超分辨率重构算法。本文算法预测得到的细节子块失真小,能很好的解决边缘保持问题,克服了传统基于学习的算法需要构造训练样本库的问题。
目前对现实三维场景的深度图像的获取越来越受到大家关注。直接获取场景深度图的设备主要有激光测距扫描仪和飞行时间(Time of Flight, TOF)相机。激光测距扫描仪可以提供准确和密集的深度信息,但是激光测距扫描仪每次只能测量一个点,过大的时间损耗限制了它们的应用。TOF相机运用高速快门,通过探测光脉冲的往返时间来得到目标物的距离,可以同时得到整幅场景的深度信息。很好的克服了激光测距扫描仪的缺陷,但是由于传感器硬件条件的限制,TOF相机得到的深度图像分辨率低,从硬件方面进行改进很难克服一些技术难题,结合图像超分辨率重建的思想从软件方面来提高深度图像的分辨率就非常有价值。
本文对深度图像的超分辨率重建算法进行研究。考虑到同场景的彩色图像和深度图像之间存在相似一致的不连续性。我们假定同场景的彩色图像和深度图像在局部小窗口内具有近似线性关系,将近似线性关系通过Matting Laplacian矩阵的方式融合到超分辨目标函数的正则约束项中,将深度图超分辨率问题转化为最优化问题来求解,提出基于Matting Laplacian矩阵的深度图超分辨率算法。由于计算Matting Laplacian矩阵非常费时,计算代价较大,接下来我们运用引导图像滤波器来描述同场景彩色图像和深度图像之间局部小窗口内的近似线性关系,提出了基于引导图像滤波的深度图超分辨率算法。考虑到同场景的彩色图像和深度图像在局部小窗口内具有的近似线性关系的假设过于简单,对一些实际的复杂场景,彩色和深度在边缘处并不完全满足这个假设,接下来我们假设同场景的彩色图像和深度图像在局部邻域内具有相同的局部结构特征,进而定义了描述这一特征的局部结构参数模型,并将该参数模型融入到正则约束项中对深度图的局部边缘结构提供约束,提出了基于彩色图像局部结构特征的深度图超分辨率算法。
Image super resolution refers to the reconstruction of a high resolution image from one or a set of blurred low resolution images. Considering that there are many cases only one low resolution image is available, so in this paper, we mainly focus on super resolution from single low resolution input image. The goal of single image super resolution is to estimate a high resolution image from a low resolution input. There are mainly three categories of approach for this problem:interpolation based methods, learning based methods, and statistical reconstruction based methods.
The interpolation based methods are simple but tend to blur the high frequency details. In this paper, we propose a new interpolation based method. Our approach uses the quad tree segmentation to partition the low resolution image, and takes the edge-directed interpolation to each segmented band of the low resolution image, and then applys a wavelet projection to optimize the high resolution image got from the local interpolation. The experimental results show our interpolation method greatly improves the image edge ringing effects of traditional interpolation algorithm.
The statistical reconstruction based methods require a probability density function of the data known as a prior image model. Maximum a Posteriori (MAP) is one of the most popular statistical methods, so MAP statistical reconstruction based methods are sparked within this research community. In this paper, we propose a novel image super resolution method based on MAP statistical reconstruction. Our approach takes the wavelet domain Implicit Markov Random Field (IMRF) model as the prior constraint and utilizes the MAP theory to construct the objective function with this model. Furthermore, we employ steepest descent method to optimize this objective function. The experimental results demonstrate that our method obtains the superior performance in comparison with traditional single image super-resolution approaches.
The learning based methods "hallucinate" high frequency details from a training set of high-resolution/low-resolution image pairs, and this kind of methods highly relies on the similarity between the training set and the test set. It is still unclear how many training examples are sufficient for the generic images. In this paper we propose a new learning based super-resolution method which base on the haar wavelet transform and back-propagation network. Considering the self-similarity between the detail subbands of Haar wavelet decomposition, firstly our approach trains the Back-propagation network to approximate the self-similarity relationship and then uses the trained network to predict the detail subbands of Haar wavelet decomposition.
Depth image of the real three-dimensional scenes get more and more of our attention. Laser range scanners can provide extremely accurate and dense three-dimensional measurement over a large working volume. But these high quality scanners measure a single point at a time and it limits their applications to static environments only. Recently new time-of-flight sensors have been developed to overcome this limitation. These sensors measure time delay between transmission of a light pulse and detection of the reflected signal on an entire frame once by using extremely faster shutter. Though this technology is promising, in the current generation, these time-of-flight sensors are expensive and very limited in terms of resolution. How to improve the resolution of the depth image is an interesting topic.
In this paper, given a low resolution depth image as input, we recover a high resolution depth image using a registered and potentially high resolution camera image of the same scene. Based on the fact that discontinuities in range and color tend to co-align, we assume that color image and depth image of the same scene have an approximately linear relationship in the locally within the small window. And we apply Matting Laplacian Matrix to exploit this linear relationship and construct an optimization problem. Furthermore, we employ steepest descent method to optimize this objective function. But, calculating the Matting Laplacian matrix is very time-consuming, so we apply the guided image filter to exploit this linear relationship between the color image and depth image of the same scene. Using guided image filter, we integrate the registered high resolution camera image into the range data and generate an initial high resolution depth image. Moreover, a reconstruction constraint is also used to further improve the quality of the initial high resolution depth image iteratively.
Taking into account that the assumption of the approximate linear relationship between color image and depth image is too simple, some practical complex scenes, color and depth at the edge do not satisfy this assumption, next we use local structural features of high resolution camera image to construct the regularization term and construct a new optimization problem. Experiments demonstrate that our approach can get excellent high resolution range image in terms of both its spatial resolution and depth precision.
本文对基于插值的超分辨率算法进行研究。考虑到传统的基于插值的超分辨方法是建立在图像的连续性假设之上的,虽然实时性非常好,但是重构的高分辨率图像在边缘上的效果不好。本文提出了基于图像局部自相似性的插值算法,我们首先对低分辨率图像进行自适应四叉树分块,然后利用图像在局部区域上表现出的自相似性,对每个局部区域的插值参数进行最小二乘估计,并据此完成插值计算,最后运用小波域投影算子对插值得到的高分辨率图像进行全局优化得到最终的高分辨率图像。本文算法大大改善了传统插值算法在图像边缘上的振铃效应,而且运算效率很高。
本文对基于最大后验概率(Maximum a Posteriori,MAP)的概率分析超分辨率算法进行研究。基于MAP的概率分析超分辨率算法可以将高分辨率目标的一些先验知识以先验概率密度函数的形式加入到优化问题的正则约束项中,将超分辨率问题转化为随机正则优化问题来求解,有非常好的理论基础,因此基于MAP的的超分辨率算法一直是一个研究的热点。本文将小波域Implicit Markov Random Field (IMRF)模型结合到图像超分辨率算法中来,构造了新的优化目标函数,提出了基于小波域IMRF模型的图像超分辨率算法。本文算法可以很好的克服小波域Hidden Markov Tree (HMT)模型超分辨率算法计算量非常大的缺陷,而且较小波域HMT超分辨率算法能得到更好的超分辨率重建结果。
本文对基于学习的超分辨率算法进行研究。基于学习的超分辨率算法需要构造低分辨率和高分辨率图像样本库,通过学习样本库得到低分辨率图像和高分辨率图像的内在联系,从而指导图像超分辨率重建。这类算法需要构造样本库,而且最终超分辨率的效果好坏很大程度上依赖于待处理图像和样本库图像之间的相似性。本文利用Haar(?)、波变换细节子块之间相近的自相似性,并运用Bp神经网络方法来估计这种自相似关系,提出了一种结合Haar小波变换和Bp神经网络的自学习的图像超分辨率重构算法。本文算法预测得到的细节子块失真小,能很好的解决边缘保持问题,克服了传统基于学习的算法需要构造训练样本库的问题。
目前对现实三维场景的深度图像的获取越来越受到大家关注。直接获取场景深度图的设备主要有激光测距扫描仪和飞行时间(Time of Flight, TOF)相机。激光测距扫描仪可以提供准确和密集的深度信息,但是激光测距扫描仪每次只能测量一个点,过大的时间损耗限制了它们的应用。TOF相机运用高速快门,通过探测光脉冲的往返时间来得到目标物的距离,可以同时得到整幅场景的深度信息。很好的克服了激光测距扫描仪的缺陷,但是由于传感器硬件条件的限制,TOF相机得到的深度图像分辨率低,从硬件方面进行改进很难克服一些技术难题,结合图像超分辨率重建的思想从软件方面来提高深度图像的分辨率就非常有价值。
本文对深度图像的超分辨率重建算法进行研究。考虑到同场景的彩色图像和深度图像之间存在相似一致的不连续性。我们假定同场景的彩色图像和深度图像在局部小窗口内具有近似线性关系,将近似线性关系通过Matting Laplacian矩阵的方式融合到超分辨目标函数的正则约束项中,将深度图超分辨率问题转化为最优化问题来求解,提出基于Matting Laplacian矩阵的深度图超分辨率算法。由于计算Matting Laplacian矩阵非常费时,计算代价较大,接下来我们运用引导图像滤波器来描述同场景彩色图像和深度图像之间局部小窗口内的近似线性关系,提出了基于引导图像滤波的深度图超分辨率算法。考虑到同场景的彩色图像和深度图像在局部小窗口内具有的近似线性关系的假设过于简单,对一些实际的复杂场景,彩色和深度在边缘处并不完全满足这个假设,接下来我们假设同场景的彩色图像和深度图像在局部邻域内具有相同的局部结构特征,进而定义了描述这一特征的局部结构参数模型,并将该参数模型融入到正则约束项中对深度图的局部边缘结构提供约束,提出了基于彩色图像局部结构特征的深度图超分辨率算法。
Image super resolution refers to the reconstruction of a high resolution image from one or a set of blurred low resolution images. Considering that there are many cases only one low resolution image is available, so in this paper, we mainly focus on super resolution from single low resolution input image. The goal of single image super resolution is to estimate a high resolution image from a low resolution input. There are mainly three categories of approach for this problem:interpolation based methods, learning based methods, and statistical reconstruction based methods.
The interpolation based methods are simple but tend to blur the high frequency details. In this paper, we propose a new interpolation based method. Our approach uses the quad tree segmentation to partition the low resolution image, and takes the edge-directed interpolation to each segmented band of the low resolution image, and then applys a wavelet projection to optimize the high resolution image got from the local interpolation. The experimental results show our interpolation method greatly improves the image edge ringing effects of traditional interpolation algorithm.
The statistical reconstruction based methods require a probability density function of the data known as a prior image model. Maximum a Posteriori (MAP) is one of the most popular statistical methods, so MAP statistical reconstruction based methods are sparked within this research community. In this paper, we propose a novel image super resolution method based on MAP statistical reconstruction. Our approach takes the wavelet domain Implicit Markov Random Field (IMRF) model as the prior constraint and utilizes the MAP theory to construct the objective function with this model. Furthermore, we employ steepest descent method to optimize this objective function. The experimental results demonstrate that our method obtains the superior performance in comparison with traditional single image super-resolution approaches.
The learning based methods "hallucinate" high frequency details from a training set of high-resolution/low-resolution image pairs, and this kind of methods highly relies on the similarity between the training set and the test set. It is still unclear how many training examples are sufficient for the generic images. In this paper we propose a new learning based super-resolution method which base on the haar wavelet transform and back-propagation network. Considering the self-similarity between the detail subbands of Haar wavelet decomposition, firstly our approach trains the Back-propagation network to approximate the self-similarity relationship and then uses the trained network to predict the detail subbands of Haar wavelet decomposition.
Depth image of the real three-dimensional scenes get more and more of our attention. Laser range scanners can provide extremely accurate and dense three-dimensional measurement over a large working volume. But these high quality scanners measure a single point at a time and it limits their applications to static environments only. Recently new time-of-flight sensors have been developed to overcome this limitation. These sensors measure time delay between transmission of a light pulse and detection of the reflected signal on an entire frame once by using extremely faster shutter. Though this technology is promising, in the current generation, these time-of-flight sensors are expensive and very limited in terms of resolution. How to improve the resolution of the depth image is an interesting topic.
In this paper, given a low resolution depth image as input, we recover a high resolution depth image using a registered and potentially high resolution camera image of the same scene. Based on the fact that discontinuities in range and color tend to co-align, we assume that color image and depth image of the same scene have an approximately linear relationship in the locally within the small window. And we apply Matting Laplacian Matrix to exploit this linear relationship and construct an optimization problem. Furthermore, we employ steepest descent method to optimize this objective function. But, calculating the Matting Laplacian matrix is very time-consuming, so we apply the guided image filter to exploit this linear relationship between the color image and depth image of the same scene. Using guided image filter, we integrate the registered high resolution camera image into the range data and generate an initial high resolution depth image. Moreover, a reconstruction constraint is also used to further improve the quality of the initial high resolution depth image iteratively.
Taking into account that the assumption of the approximate linear relationship between color image and depth image is too simple, some practical complex scenes, color and depth at the edge do not satisfy this assumption, next we use local structural features of high resolution camera image to construct the regularization term and construct a new optimization problem. Experiments demonstrate that our approach can get excellent high resolution range image in terms of both its spatial resolution and depth precision.
引文
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