图像超分辨率论文：基于改进的稀疏表示的图像超分辨率方法.docVIP

图像超分辨率论文：基于改进的稀疏表示的图像超分辨率方法 【中文摘要】图像超分辨率的目标是将低分辨率图像转化为高分辨率图像。通过放大目标图像的感兴趣区域，从而更加容易地辨别出感兴趣的目标物体或人物。该技术被广泛应用于视频监控、卫星图像分析、医学诊断、高清电视等领域。目前，基于稀疏表示的方法是该领域中较先进的方法，但其在图像效果和处理速度上仍存在不足之处。本文在基于稀疏表示的图像超分辨率方法的基础上，围绕着如何提高重构的图像效果和处理速度展开研究。首先，从理论上分析了影响Feature-sign算法效率的因素，实验结果证明可以通过降低字典维度和提供稀疏系数初始值两种方式来提高Feature-sign算法效率。然后，本文提出了基于特征聚类的图像超分辨率方法。针对单一特征不能有效地重构边缘轮廓的不足，提出有监督性的特征提取方法。该方法使用由Canny算法提取得到的二值边缘图作为标记，分别从一阶和二阶梯度图像中提取边缘特征和纹理特征。通过分别使用边缘和纹理特征，初步改善了图像效果和提高了处理速度。针对处理速度慢的不足，提出基于特征聚类的方法。该方法能够得到结构性更强、维度更低的子字典。因此，可以为输入的低分辨率图像块选择最相关的子字典进行重构，继而提高处理速度和改善图像效果。最后，本文提出了有重叠聚类的方法以平衡子字典间的独立性和兼容性，解决了因聚类而造成重构信息分散的问题。实验表明，本文提出的方法不仅改善了重构图像的效果，还地提高了处理的速度。 【英文摘要】The objective of Image Super-Resolution (SR) is to produce a high-resolutionimage from the low-resolution image which captured from the same scene. Thistechnique makes it easier to distinguish the interested target objects or persons viazoom out the interested region of target image.This technique is widely used in manyvisual applications, such as surveillance, satellite image analysis, medical diagnostics,and high-definition TV etc. Although sparse representation based image SR is one ofthe best approaches in this field, it still insufficient in image quality and processingspeed.This paper mainly focuses on improving the quality of the reconstructed imageand the processing speed, based on the image SR via sparse representation method.Firstly, Theoretical analysis on the factors which effect the efficiency of thefeature-sign algorithm and the experimental results show that we can speed-up thealgorithm by reduced the dictionary size or given the initial value of the sparsecoefficient. Then, this paper proposesa SR method based on feature classification. Asthe single feature cannot reconstruct the image contour efficiently, one supervisedfeature extraction approach was proposed to extract the edge and texture features fromthe first-order and second-order gradient images marked by the binary edge imagebycanny algorithm. Using the dual features, w