Multi-frame super-resolution using adaptive normalized convolution

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• 作者：K. Joseph Abraham Sundar ; V. Vaithiyanathan
• 关键词：Super ; resolution ; Image fusion ; Normalized convolution ; Adaptive normalized convolution ; Applicability function
• 刊名：Signal, Image and Video Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：11
• 期：2
• 页码：357-362
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems;
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：11

文摘

An enhanced fusion algorithm for generating a super-resolution image from a sequence of low-resolution images captured from identical scene apparently a video based on adaptive normalized convolution has been designed and analyzed. The algorithm for fusing the images is based on the supporting structure of normalized convolution. Here the idea is projection of local signals onto a subspace. The adaptive nature of the window function in adaptive normalized convolution helps to gather more samples for processing and increases signal-to-noise ratio, decreases diffusion through discontinuities. The validation of proposed method is done using simulated experiments and real-time experiments. These experimental results are compared with various latest techniques using performance measures like peak signal-to-noise ratio, sharpness index and blind image quality index. In both the cases of experiments, the proposed adaptive normalized convolution-based super-resolution image reconstruction has proved to be highly efficient which is needed for satellite imaging, medical imaging diagnosis, military surveillance, remote sensing, etc.