图像及视频超分辨率重建技术研究
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摘要
高分辨率图像能提供目标场景更多的细节信息,对于图像的分析和处理有重要作用。随着视频和图像处理技术应用的普及和快速增长,人们对视频和图像序列的高分辨率要求越来越高,这对感光器制造技术提出了新的挑战。然而,当前通过减小单位像素尺寸或增大感光器芯片尺寸的硬件方案都存在技术上的瓶颈,同时价格昂贵的高精密感光器不适合于普及应用。因此,在现有条件限制下,如何提高图像和视频序列的空间分辨率,就成为人们研究的重点。超分辨率重建技术就在这种背景下应运而生。该项技术通过信号处理的软件方法实现图像空间分辨率的提升,它利用多幅关于同一场景的具有亚像素级位移的低分辨率图像来重建高分辨率图像。超分辨率重建可突破图像采集设备的分辨率限制,充分利用多帧低分辨率图像之间的互补信息,实现亚像素级的图像信息融合。利用此项技术能够在不升级现有采集设备的情况下,提高采集图像的分辨率；也能够在不增加传输信号带宽的情况下,改善图像或视频的画面质量。因此,超分辨率重建技术在遥感、军事、医学成像等领域有很广泛的应用前景,当前,超分辨率重建技术己成为图像处理领域最热门的研究内容。随着研究的加深,超分辨率研究范围已经从传统的原始图像序列拓展到压缩视频,从空间分辨率拓展到时间分辨率,从单视点视频拓展到多视点视频。
本文对超分辨率重建技术中的若干关键问题进行了研究,主要集中在图像配准、立体图像视差估计、立体视频的空域／时域超分辨率重建等方面。具体工作和研究成果如下：
1、超分辨率重建通常包含图像配准、插值、复原及后处理等操作,图像配准是其中最为基本的一步,它的性能直接影响到图像超分辨率重建的效果。本文提出一种结合小波多分辨率分解、NEDI插值和三步法块匹配的亚像素配准方法,在保证配准精度的同时,可大大减少搜索量,提高配准速度。
2、基于传统香农熵的互信息是一个定量测度,它以图像中各种灰度值出现的概率为出发点,只考虑了两幅图像中对应像素的关系,忽略了水平面上像素之间的空间相关性,使得配准的峰值不够尖锐,配准的最佳位置难于确定。本文提出一种结合图像梯度、方差和归一化互信息的相似性测度,有效融合图像定性的空间位置关系和定量的灰度统计信息,缓解了基于单一互信息测度配准容易出现的局部极值问题,同时对噪声图像具有一定的鲁棒性。
3、提出一种结合自适应加权和互信息准则的局部立体匹配方法,将前者在代价聚合中的优势,以及后者在代价计算中的优势联合起来,有效提高了立体匹配的抗辐射干扰性能力。实验结果表明,本文所提出的立体匹配算法获得的视差精度与当前优秀的局部算法精度相当,尤其能够很好地处理现有算法难以解决的各类光照差异问题,具有较鲁棒的抗干扰的能力,对于噪声等辐射畸变也有较高的准确性和适应性。
4、结合分布式视频编码架构,提出了一种空时超分辨率重建方法,通过挖掘立体视频在时间／视点间的冗余信息,实现了对低帧率序列的帧率提升和低空间分辨率序列的分辨率增强,实验结果表明,所提出的超分辨率重建方法在主观视觉和客观性能方面都取得了较好的效果。
A high quality image always contains further detailed information of targets, and it is of great value for analysis and post-process. Along with the rapid improvements of video and image processing technologies in recent years, the demand for high resolution video and image sequences grows fast, which gradually raises new challenges for manufacturing technology on image sensors. At present, however, it is impossible or hard to break the bottleneck by hardware schemes, such as reducing the pixel size or increasing the chip size. In the meanwhile, prices of expensive high precision sensor are not applicable to wider applications. So, how to enhance the spatial resolution of video and image sequences under these limitations becomes an active research topic. The technique of super-resolution (SR) reconstruction is developed under this circumstance. Super-resolution reconstruction refers to a software technique that enhances the resolution of images or videos by using digital signal processing technology. It reconstructs high resolution and high quality image(s) from a group of degraded low resolution images with sub-pixel shifts from the same scene. It breaks though the resolution limit of image acquisition equipment and can achieve data fusion on sub-pixel level by wealthy complementary information. Utilizing SR techniques can improve the resolution of images without updating the existing low-resolution sampling devices; and also can enjoy high-quality videos without increasing the signal transmission bandwidth. SR reconstruction processing has proved to be useful in many practical applications, such as remote sensing, military detection, medical image, etc. Currently, image super-resolution reconstruction has become one of the hottest areas of image processing. With the deepening of the research, the range of this technique has expanded from traditional original image sequence to compressed video, from spatial resolution to temporal resolution, from mono-view video to multi-view video.
The dissertation investigates several key issues of super-resolution of video and image sequences including image registration, stereo matching and spatio-temporal super-resolution for stereo video. The main contributions and innovation points of the dissertation are as follows:
1. Super-resolution reconstruction usually consists of three steps:registration, reconstruction and restoration. As the necessary step of super-resolution reconstruction, the performance of the registration algorithm greatly affects the quality of the reconstructed images. This paper proposed a sub-pixel registration method using a combination of wavelet-based multi-resolution decomposition, NEDI interpolation and three-step search block-matching algorithm. The proposed approach can greatly reduce the search volume and accelerate the speed of the registration, while maintaining registration accuracy.
2. Mutual information is a quantitative criteria based on the traditional Shannon entropy. It takes into account only the relationships between corresponding individual pixels and not those of each pixel's neighborhood, such that the alignment of the peak is not sharp, the best alignment position is difficult to locate. We propose a new similarity metric, which combines mutual information and a weighting function based on image gradient and image variance. This method effectively joints qualitative spatial positional relationship and quantitative statistical information of grayscale. The proposed similarity measurement alleviates the local minima problem and is more robust to noise than only with mutual information.
3. We propose a new local stereo matching approach of combined adaptive weight and mutual information, which incorporating the advantage of the former in cost aggregation, as well as the advantage of the latter in cost computation. The experimental results show that the accuracy obtained by our proposed stereo matching algorithm is almost as excellent as state of the art local algorithm. Especially, this method can effectively improve the performance with kinds of radiometric differences, which is difficult to solve by other existing algorithms.
4. We propose a spatio-temporal super-resolution reconstruction method for a distributed multi-view video coding architecture. The super-resolution scheme achieves temporal resolution enhancement and spatial resolution enhancement for mixed resolution video by exploiting inter-view/temporal correlation. Simulation results indicate that the super-resolution method has achieved good results in subjective visual and objective performance.
本文对超分辨率重建技术中的若干关键问题进行了研究,主要集中在图像配准、立体图像视差估计、立体视频的空域／时域超分辨率重建等方面。具体工作和研究成果如下：
1、超分辨率重建通常包含图像配准、插值、复原及后处理等操作,图像配准是其中最为基本的一步,它的性能直接影响到图像超分辨率重建的效果。本文提出一种结合小波多分辨率分解、NEDI插值和三步法块匹配的亚像素配准方法,在保证配准精度的同时,可大大减少搜索量,提高配准速度。
2、基于传统香农熵的互信息是一个定量测度,它以图像中各种灰度值出现的概率为出发点,只考虑了两幅图像中对应像素的关系,忽略了水平面上像素之间的空间相关性,使得配准的峰值不够尖锐,配准的最佳位置难于确定。本文提出一种结合图像梯度、方差和归一化互信息的相似性测度,有效融合图像定性的空间位置关系和定量的灰度统计信息,缓解了基于单一互信息测度配准容易出现的局部极值问题,同时对噪声图像具有一定的鲁棒性。
3、提出一种结合自适应加权和互信息准则的局部立体匹配方法,将前者在代价聚合中的优势,以及后者在代价计算中的优势联合起来,有效提高了立体匹配的抗辐射干扰性能力。实验结果表明,本文所提出的立体匹配算法获得的视差精度与当前优秀的局部算法精度相当,尤其能够很好地处理现有算法难以解决的各类光照差异问题,具有较鲁棒的抗干扰的能力,对于噪声等辐射畸变也有较高的准确性和适应性。
4、结合分布式视频编码架构,提出了一种空时超分辨率重建方法,通过挖掘立体视频在时间／视点间的冗余信息,实现了对低帧率序列的帧率提升和低空间分辨率序列的分辨率增强,实验结果表明,所提出的超分辨率重建方法在主观视觉和客观性能方面都取得了较好的效果。
A high quality image always contains further detailed information of targets, and it is of great value for analysis and post-process. Along with the rapid improvements of video and image processing technologies in recent years, the demand for high resolution video and image sequences grows fast, which gradually raises new challenges for manufacturing technology on image sensors. At present, however, it is impossible or hard to break the bottleneck by hardware schemes, such as reducing the pixel size or increasing the chip size. In the meanwhile, prices of expensive high precision sensor are not applicable to wider applications. So, how to enhance the spatial resolution of video and image sequences under these limitations becomes an active research topic. The technique of super-resolution (SR) reconstruction is developed under this circumstance. Super-resolution reconstruction refers to a software technique that enhances the resolution of images or videos by using digital signal processing technology. It reconstructs high resolution and high quality image(s) from a group of degraded low resolution images with sub-pixel shifts from the same scene. It breaks though the resolution limit of image acquisition equipment and can achieve data fusion on sub-pixel level by wealthy complementary information. Utilizing SR techniques can improve the resolution of images without updating the existing low-resolution sampling devices; and also can enjoy high-quality videos without increasing the signal transmission bandwidth. SR reconstruction processing has proved to be useful in many practical applications, such as remote sensing, military detection, medical image, etc. Currently, image super-resolution reconstruction has become one of the hottest areas of image processing. With the deepening of the research, the range of this technique has expanded from traditional original image sequence to compressed video, from spatial resolution to temporal resolution, from mono-view video to multi-view video.
The dissertation investigates several key issues of super-resolution of video and image sequences including image registration, stereo matching and spatio-temporal super-resolution for stereo video. The main contributions and innovation points of the dissertation are as follows:
1. Super-resolution reconstruction usually consists of three steps:registration, reconstruction and restoration. As the necessary step of super-resolution reconstruction, the performance of the registration algorithm greatly affects the quality of the reconstructed images. This paper proposed a sub-pixel registration method using a combination of wavelet-based multi-resolution decomposition, NEDI interpolation and three-step search block-matching algorithm. The proposed approach can greatly reduce the search volume and accelerate the speed of the registration, while maintaining registration accuracy.
2. Mutual information is a quantitative criteria based on the traditional Shannon entropy. It takes into account only the relationships between corresponding individual pixels and not those of each pixel's neighborhood, such that the alignment of the peak is not sharp, the best alignment position is difficult to locate. We propose a new similarity metric, which combines mutual information and a weighting function based on image gradient and image variance. This method effectively joints qualitative spatial positional relationship and quantitative statistical information of grayscale. The proposed similarity measurement alleviates the local minima problem and is more robust to noise than only with mutual information.
3. We propose a new local stereo matching approach of combined adaptive weight and mutual information, which incorporating the advantage of the former in cost aggregation, as well as the advantage of the latter in cost computation. The experimental results show that the accuracy obtained by our proposed stereo matching algorithm is almost as excellent as state of the art local algorithm. Especially, this method can effectively improve the performance with kinds of radiometric differences, which is difficult to solve by other existing algorithms.
4. We propose a spatio-temporal super-resolution reconstruction method for a distributed multi-view video coding architecture. The super-resolution scheme achieves temporal resolution enhancement and spatial resolution enhancement for mixed resolution video by exploiting inter-view/temporal correlation. Simulation results indicate that the super-resolution method has achieved good results in subjective visual and objective performance.
引文
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