Gradient-___domain image processing: Difference between revisions

Revision as of 19:12, 12 August 2014

Revision as of 19:37, 12 August 2014

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== Image Editing ==

For [[image editing]] purposes, the gradient is obtained from an existing image and modified. Various methodsoperators, such as a[[finite difference]] or [[Sobel operator|Sobel]], can be used to find the gradient of a given image. This gradient can then be manipulated directly to produce a number of different effects when the resulting image is solved for. For example, if the gradient is scaled by a uniform constant it results in a simple sharpening filter. A better sharpening filter can be made by only scaling the gradient in areas deemed important.<ref name="Bhat2010" />

Other uses include:

* seamless [[image stitching]]<ref>Levin, Anat, et al. "Seamless image stitching in the gradient ___domain." Computer Vision-ECCV 2004. Springer Berlin Heidelberg, 2004. 377-389.</ref>

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* the ability to seamlessly clone one part of an image onto another in ways that are difficult to achieve with conventional image-___domain techniques.<ref name="Perez2003" />

* [[High-dynamic-range imaging]]<ref>Fattal, Raanan, Dani Lischinski, and Michael Werman. "Gradient ___domain high dynamic range compression." ACM Transactions on Graphics (TOG). Vol. 21. No. 3. ACM, 2002.</ref>

GradientThese gradient ___domain editing techniques can also be extended to moving images, by considering a video clip to be a cube of pixels and solving a 3d Poisson equation.<ref>Wang, Hongcheng, et al. "Videoshop: A new framework for spatio-temporal video editing in gradient ___domain." Graphical models 69.1 (2007): 57-70.</ref>

== Seamless Image Cloning ==