Super-Resolution (SR) processing is a technique that produces a High - Resolution (HR) image from a range of Low - Resolution (LR) images. There are many methods for implementing the SR signal processing. In this pape...
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ISBN:
(纸本)9781467356046
Super-Resolution (SR) processing is a technique that produces a High - Resolution (HR) image from a range of Low - Resolution (LR) images. There are many methods for implementing the SR signal processing. In this paper, we introduce a specific transformation that were developed in the recent years called Curvelet Transform (CT), and then apply it in SR processing to obtain a quality improvement of SR images. We discuss two separate algorithms using the Discrete Curvelet Transform (DCT) and then compare them based on the results of HR images. We find out that the first algorithm, named the iterative algorithm can produce better HR images than the second one, the Interpolation algorithm. However, the iterative algorithm also take more computational cost than that of the Interpolation Algorithm. It is a reasonable trade-off between images quality and processing speed. We also made a comparison between our algorithms and previous works such as the Projection onto Convex Sets (POCS) and the Nearest Neighbourhood algorithms to show the quality improvement.
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