基于非局部稀疏的图像去噪与平滑方法研究
发布时间：2018-09-10 19:19
【摘要】：计算机视觉的研究目标是通过图像或者视频理解场景,图像处理技术是实现该目标的关键技术。图像去噪和平滑是图像处理领域的基础问题。在现实生活中,由于传感器受环境影响、传输信道被干扰等原因,在图像获取与传输的过程中,噪声被不可避免的引入,从而造成图像失真。图像失真现象必然会对图像特征提取、场景理解等后续工作造成干扰,从而影响计算机智能处理任务的准确性。图像去噪的研究目标是将噪声从有噪图像中分离出来,更好地还原图像的真实信息,特别是边缘和纹理细节。如果能够对干净图像建立较好的表达模型,就可以保留更多的有用信息,保证复原图像的准确性。图像平滑的目标则是通过分离图像的结构与细节,用来提取对于人类视觉感知最重要的结构边缘,能够为更高级的计算机视觉任务打下坚实的基础。图像去噪和图像平滑作为基础的图像处理技术,在空间项目、医学、考古学、工业机器视觉、军事识别、卫星图像处理等领域有着广泛的应用。但是由于图像内容复杂多变,图像去噪和图像平滑的研究面临一系列问题和挑战。首先,依靠现有数学工具还无法准确地描述图像,现有方法基于各种假设建立图像的表达模型,存在一定的局限性；其次,人类视觉系统的工作机制非常复杂,目前对于人类感知原理的研究还处于初级阶段,因此无法以明确的数学模型定义一幅图像中具有视觉意义的特征。本文围绕图像去噪和平滑问题的研究热点和难点展开研究,提出基于非局部稀疏的图像处理方法。新方法能够充分利用自然图像本身的有用信息,有效弥补了数学模型的缺陷,以数据驱动的方式大大提高了图像去噪和平滑的效果。主要工作包括：1.提出了基于PCA字典的自适应稀疏编码去噪方法。通过分析PCA字典上稀疏编码误差的统计特性,采用拉普拉斯函数近似编码误差的分布,基于后验估计理论提出一个新的非局部稀疏编码模型。新模型中用于平衡保真项与非局部约束项的正则化参数是自适应确定的。为获得可靠的稀疏编码估计,提出了基于滤波的迭代收缩算法。滤波可以有效抑制后向投影过程的噪声,进一步得到稀疏编码的鲁棒估计。新方法有效提高了编码准确率,从而取得很好的纹理保留和噪声去除效果。2.提出了基于低秩和梯度稀疏的图像平滑方法。通过对自然图像结构和纹理特征的分析,基于自然图像非局部自相似性提出一个图像块分组低秩先验,然后结合平滑图像的全局梯度稀疏先验提出一种新的图像平滑优化方法。低秩先验项约束了平滑图像中相似分组内部各图像块的强相关性,可以去除小尺度噪点和细节、保持细长结构边缘,保证一致的平滑效果。针对新的目标能量函数的优化问题,给出了基于交替迭代近似求解算法的详细流程。新方法能够达到一致性较高的平滑效果,在去除细节的同时保持重要的结构边缘。3.提出了非局部梯度聚集图像平滑方法。通过分析自然图像梯度图的特点,基于非局部自相似性提出平滑图像梯度图的非局部聚集约束项,将该约束与梯度L0范数最小化先验结合得到一个新的优化框架。然后给出了交替迭代算法用于高效求解新能量模型的优化问题。新方法的非局部约束以数据驱动的方式削弱了相似块之间梯度的不一致性,能够有效地去除复杂区域的细节、保持对比度不明显的有意义结构。与现有方法相比,新方法的平滑结果不仅一致性高,而且能够保持结构边缘不移位。4.研究了图像平滑在智能图像处理中的应用。平滑图像在去除琐碎细节的同时保留了对于人类视觉系统非常关键的结构信息,对于内容相关的图像处理问题具有很强的应用价值。首先研究了图像平滑在图像放缩、图像编辑等问题中的应用。然后基于平滑方法提出一种新的多尺度空间构造方法,并研究了多尺度空间在显著性检测问题中的应用。实验结果显示图像平滑本身和多尺度空间在各类应用中都起到了很好的提升作用。
[Abstract]:The research goal of computer vision is to understand the scene through image or video. Image processing technology is the key technology to achieve this goal. Image denoising and smoothing are the basic problems in the field of image processing. Noise is unavoidably introduced into the image, resulting in image distortion. Image distortion will inevitably interfere with the follow-up work of image feature extraction and scene understanding, thus affecting the accuracy of computer intelligent processing tasks. Information, especially edges and texture details. If a good representation model can be established for a clean image, more useful information can be retained to ensure the accuracy of the restored image. Advanced computer vision tasks lay a solid foundation. Image denoising and image smoothing as the basis of image processing technology, in space projects, medicine, archaeology, industrial machine vision, military recognition, satellite image processing and other fields have a wide range of applications. There are a series of problems and challenges. Firstly, relying on the existing mathematical tools can not accurately describe the image, the existing methods based on various assumptions to establish the image representation model, there are certain limitations; secondly, the human visual system working mechanism is very complex, the current research on the principle of human perception is still in its infancy. It is impossible to define visually meaningful features in an image by a definite mathematical model. This paper focuses on the research hotspots and difficulties of image denoising and smoothing, and proposes an image processing method based on non-local sparseness. The main work includes: 1. An adaptive sparse coding denoising method based on PCA dictionary is proposed. By analyzing the statistical characteristics of sparse coding errors in PCA dictionary, Laplace function is used to approximate the distribution of coding errors and a posterior estimation theory is proposed. A new non-local sparse coding model is proposed in which the regularization parameters for balancing fidelity terms and non-local constraints are determined adaptively. To obtain reliable sparse coding estimation, an iterative shrinkage algorithm based on filtering is proposed. Filtering can effectively suppress the noise in the backward projection process and further obtain sparse coding. Robust estimation. The new method effectively improves the coding accuracy and achieves good texture preservation and noise removal. 2. An image smoothing method based on low rank sum gradient sparseness is proposed. Then a new image smoothing optimization method is proposed based on the global gradient sparse priori of smoothing image. The low rank priori constrains the strong correlation of each image block in the similar grouping of smoothing image. It can remove the small scale noise and details, maintain the edge of slender structure and ensure the consistent smoothing effect. A detailed flow chart based on alternating iteration approximation algorithm is given to solve the optimization problem. The new method can achieve high uniform smoothing effect and maintain important structural edges while removing details. 3. A non-local gradient clustering image smoothing method is proposed. The characteristics of natural image gradient map are analyzed and non-local self-similarity is used. A non-local aggregation constraint term for smoothing image gradient graph is proposed, which is combined with the L0 norm minimization prior to obtain a new optimization framework. Then an alternating iteration algorithm is presented to efficiently solve the optimization problem of the new energy model. The non-local constraints of the new method weaken the ladder between similar blocks in a data-driven manner. Compared with the existing methods, the smoothing results of the new method are not only consistent, but also can keep the edges of the structure not shifting. 4. The application of image smoothing in intelligent image processing is studied. At the same time, it retains the structure information which is very important to human visual system and has a strong application value for content-related image processing. Firstly, the application of image smoothing in image zooming and zooming, image editing and other issues is studied. Then, a new multi-scale space construction method based on smoothing method is proposed and studied. The experimental results show that the image smoothing itself and the multi-scale space play a very good role in various applications.
【学位授予单位】：山东大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP391.41
，
本文编号：2235380
[Abstract]:The research goal of computer vision is to understand the scene through image or video. Image processing technology is the key technology to achieve this goal. Image denoising and smoothing are the basic problems in the field of image processing. Noise is unavoidably introduced into the image, resulting in image distortion. Image distortion will inevitably interfere with the follow-up work of image feature extraction and scene understanding, thus affecting the accuracy of computer intelligent processing tasks. Information, especially edges and texture details. If a good representation model can be established for a clean image, more useful information can be retained to ensure the accuracy of the restored image. Advanced computer vision tasks lay a solid foundation. Image denoising and image smoothing as the basis of image processing technology, in space projects, medicine, archaeology, industrial machine vision, military recognition, satellite image processing and other fields have a wide range of applications. There are a series of problems and challenges. Firstly, relying on the existing mathematical tools can not accurately describe the image, the existing methods based on various assumptions to establish the image representation model, there are certain limitations; secondly, the human visual system working mechanism is very complex, the current research on the principle of human perception is still in its infancy. It is impossible to define visually meaningful features in an image by a definite mathematical model. This paper focuses on the research hotspots and difficulties of image denoising and smoothing, and proposes an image processing method based on non-local sparseness. The main work includes: 1. An adaptive sparse coding denoising method based on PCA dictionary is proposed. By analyzing the statistical characteristics of sparse coding errors in PCA dictionary, Laplace function is used to approximate the distribution of coding errors and a posterior estimation theory is proposed. A new non-local sparse coding model is proposed in which the regularization parameters for balancing fidelity terms and non-local constraints are determined adaptively. To obtain reliable sparse coding estimation, an iterative shrinkage algorithm based on filtering is proposed. Filtering can effectively suppress the noise in the backward projection process and further obtain sparse coding. Robust estimation. The new method effectively improves the coding accuracy and achieves good texture preservation and noise removal. 2. An image smoothing method based on low rank sum gradient sparseness is proposed. Then a new image smoothing optimization method is proposed based on the global gradient sparse priori of smoothing image. The low rank priori constrains the strong correlation of each image block in the similar grouping of smoothing image. It can remove the small scale noise and details, maintain the edge of slender structure and ensure the consistent smoothing effect. A detailed flow chart based on alternating iteration approximation algorithm is given to solve the optimization problem. The new method can achieve high uniform smoothing effect and maintain important structural edges while removing details. 3. A non-local gradient clustering image smoothing method is proposed. The characteristics of natural image gradient map are analyzed and non-local self-similarity is used. A non-local aggregation constraint term for smoothing image gradient graph is proposed, which is combined with the L0 norm minimization prior to obtain a new optimization framework. Then an alternating iteration algorithm is presented to efficiently solve the optimization problem of the new energy model. The non-local constraints of the new method weaken the ladder between similar blocks in a data-driven manner. Compared with the existing methods, the smoothing results of the new method are not only consistent, but also can keep the edges of the structure not shifting. 4. The application of image smoothing in intelligent image processing is studied. At the same time, it retains the structure information which is very important to human visual system and has a strong application value for content-related image processing. Firstly, the application of image smoothing in image zooming and zooming, image editing and other issues is studied. Then, a new multi-scale space construction method based on smoothing method is proposed and studied. The experimental results show that the image smoothing itself and the multi-scale space play a very good role in various applications.
【学位授予单位】：山东大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP391.41
，
本文编号：2235380
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