Blurriness-Guided Unsharp Masking

反锐化掩蔽 像素 遮罩(插图) 计算机科学 人工智能 滤波器(信号处理) 计算机视觉 噪音(视频) 图像(数学) 降噪 非本地手段 图像复原 GSM演进的增强数据速率 模式识别(心理学) 图像增强 图像处理 视觉艺术 艺术
作者
Wei Ye,Kai‐Kuang Ma
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:27 (9): 4465-4477 被引量:34
标识
DOI:10.1109/tip.2018.2838660
摘要

In this paper, a highly-adaptive unsharp masking (UM) method is proposed and called the blurriness-guided UM, or BUM, in short. The proposed BUM exploits the estimated local blurriness as the guidance information to perform pixel-wise enhancement. The consideration of local blurriness is motivated by the fact that enhancing a highly-sharp or a highly-blurred image region is undesirable, since this could easily yield unpleasant image artifacts due to over-enhancement or noise enhancement, respectively. Our proposed BUM algorithm has two powerful adaptations as follows. First, the enhancement strength is adjusted for each pixel on the input image according to the degree of local blurriness measured at the local region of this pixel's location. All such measurements collectively form the blurriness map, from which the scaling matrix can be obtained using our proposed mapping process. Second, we also consider the type of layer-decomposition filter exploited for generating the base layer and the detail layer, since this consideration would effectively help to prevent over-enhancement artifacts. In this paper, the layer-decomposition filter is considered from the viewpoint of edge-preserving type versus non-edge-preserving type. Extensive simulations experimented on various test images have clearly demonstrated that our proposed BUM is able to consistently yield superior enhanced images with better perceptual quality to that of using a fixed enhancement strength or other state-of-the-art adaptive UM methods.
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