Image enhancement via adaptive unsharp masking

By Andrea Polesel, Giovanni Ramponi, J. Mathews. IEEE Transactions on Image Processing, 2000

870 citations recorded by the source corpus.

How researchers see this paper

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Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (2012, methodology): “Among the existing enhancement techniques, we mention methods based on histogram manipulation [35], linear and nonlinear unsharp masking [36]–[38], fuzzy logic [39], and weighted median filter [40], [41].”
A Generalized Unsharp Masking Algorithm (2011, background): “Adaptive gain control has also been studied [5].”
Artificial intelligence in medicine (1989, positive): “In this paper we use the technique presented in [50] to”
Image enhancement using a contrast measure in the compressed domain (2003, background): “Each of these algorithms can be classified into two types of image enhancement methods [4]: indirect image enhancement methods and direct image enhancement methods.”
Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement (2011, background): “The most popular approach for image enhancement is the unsharp masking (UM) technique [37].”
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions (2021, neutral): “To appropriately enhance an image, some traditional meth-ods (Polesel, Ramponi, and Mathews 2000; Yu and Bajaj 2004; Wang et al. 2021) adaptively calculate the parameters of image transformation according to the corresponding image features.”