Abstract

In this paper, we have proposed a novel method for image super-resolution using single image. Based on structural similarity index, an image similar to input low-resolution (LR) image is selected from the database and two separate external dictionaries i.e. smooth and textured, are formed from the selected image based on their variances. Different features are used for representation of different type of patches. For smooth patches norm luminance is used as feature vector and for textured patches it consist of first and second order gradients. In neighbor embedding process, a new parameter in combination with Euclidean distance has been introduced to eliminate outliers. Extensive simulations are performed to show superiority of the method.

中文翻译：

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基于方差的外部字典可改善单图像超分辨率

摘要

在本文中，我们提出了一种使用单幅图像进行超分辨率的新方法。基于结构相似性索引，从数据库中选择与输入的低分辨率（LR）图像相似的图像，并根据它们的差异从所选图像中形成两个独立的外部字典，即平滑和纹理化字典。不同的功能用于表示不同类型的补丁。对于平滑补丁，标准亮度被用作特征向量，对于纹理补丁，它由一阶和二阶梯度组成。在邻居嵌入过程中，结合欧几里德距离引入了一个新参数，以消除异常值。进行了广泛的仿真以显示该方法的优越性。