Fast Image Super-Resolution via Local Adaptive Gradient Field Sharpening Transform
Fast Image Super-Resolution via Local Adaptive Gradient Field Sharpening Transform
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DOI：
10.1109/tip.2017.2789323
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发表时间：
2018-01
影响因子：
10.6
通讯作者：
Qiang Song;Ruiqin Xiong;Dong Liu;Zhiwei Xiong;Feng Wu;Wen Gao
中科院分区：
文献类型：
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作者：
Qiang Song;Ruiqin Xiong;Dong Liu;Zhiwei Xiong;Feng Wu;Wen Gao
本文提出了一种单图像超分辨率方案，通过引入梯度场锐化变换，将上采样的低分辨率（LR）图像的模糊梯度场转换为原始高分辨率（HR）图像的更清晰的梯度场。与需要找出整个梯度轮廓结构并定位边缘点的现有方法不同，我们提出了一种仅基于小邻域中的像素自适应地锐化梯度场的新方法。为了保持图像对比度，自适应缩放图像梯度以保持梯度场积分稳定。最后，通过将 LR 图像与锐化的 HR 梯度场融合来重建 HR 图像。实验结果表明，该算法可以生成更准确的梯度场并产生具有更好客观和视觉质量的超分辨率图像。另一个优点是所提出的梯度锐化变换非常快并且适合低复杂度应用。
This paper proposes a single-image super-resolution scheme by introducing a gradient field sharpening transform that converts the blurry gradient field of upsampled low-resolution (LR) image to a much sharper gradient field of original high-resolution (HR) image. Different from the existing methods that need to figure out the whole gradient profile structure and locate the edge points, we derive a new approach that sharpens the gradient field adaptively only based on the pixels in a small neighborhood. To maintain image contrast, image gradient is adaptively scaled to keep the integral of gradient field stable. Finally, the HR image is reconstructed by fusing the LR image with the sharpened HR gradient field. Experimental results demonstrate that the proposed algorithm can generate more accurate gradient field and produce super-resolved images with better objective and visual qualities. Another advantage is that the proposed gradient sharpening transform is very fast and suitable for low-complexity applications.