利用在线字典学习实现图像超分辨率重建的算法

图像超分辨率重建是通过对单张或多张具有互补信息的低分辨率图像进行处理，重建一张高分辨率图像的技术。在单张图像的超分辨率重建中，基于稀疏表示的方法取得了很好的效果，得到了广泛的应用。一张图像中不同区域的图像块的内容一般会有显著变化。而基于稀疏表示的超分辨率重建算法多采用固定的字典，无法适应每一个图像块的重建需求。提出了一种结合外部数据和输入图像自身信息进行超分辨率重建的方法，通过搜索待处理图像块的非局部自相似块，结合在线字典学习方法对字典进行更新，从而保证更新后的字典能够匹配待处理的图像块。采用包括遥感图像在内的5张图像进行实验，并与4种经典的超分辨率重建算法进行比较，实验结果表明，此算法在主观评价和客观评价方面都有更好的表现。

Image Super-resolution with On-line Dictionary Learning

Image super-resolution reconstruction is the method that uses one or several low-resolution images to reconstruct a high-resolution image. Sparse representation has been widely used in single image super-resolution reconstruction. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries, which common super-resolution algorithms based on sparse representation often used, cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which trains the dictionary with external database and the input low-resolution image itself. With the nonlocal similar patches extracted from the input image, the dictionary is updated by on-line dictionary learning method to ensure that the new dictionary is suitable for every patch in the image. Extensive experiments on natural images and remote sensing images show that the method with on-line dictionary learning achieves better results than those of the state-of-the-art algorithms in terms of both objective and visual evaluations.