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A new method for super-resolution reconstruction using wavelet transform

机译:小波变换的超分辨率重建新方法

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In this paper, a super-resolution reconstruction algorithm based on Projection onto Convex Sets (POCS) and wavelets transform is proposed. A high resolution image, after wavelet transform, can be decomposed into two parts: approximate subband and detail subbands. Under some special conditions, the blurred low-resolution images can be thought as the wavelets transform approximate subbands of a high resolution image. Based on the above relationship, we can construct a series of convex sets and then apply the POCS method to recovering high resolution image based on the convex sets. After finite iterative computation, the desired high solution image can be obtained. The experimental results show that the algorithm has good performance in super-resolution reconstruction when the magnification is large enough.
机译:提出了一种基于凸集投影和小波变换的超分辨率重建算法。经过小波变换的高分辨率图像可以分解为两部分:近似子带和细节子带。在某些特殊条件下,模糊的低分辨率图像可以看作是小波变换高分辨率图像的近似子带。基于上述关系,我们可以构造一系列凸集,然后将POCS方法应用于基于凸集的高分辨率图像的恢复。经过有限的迭代计算,可以获得所需的高分辨率图像。实验结果表明,当放大倍数足够大时,该算法在超分辨率重建中具有良好的性能。

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