Editing Unsharp masking (section)

== Comparison with deconvolution ==
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For image processing, [[deconvolution]] is the process of approximately inverting the process that caused an image to be blurred. Specifically, unsharp masking is a simple linear image operation—a [[convolution]] by a [[kernel (image processing)|kernel]] that is the [[Dirac delta]] minus a gaussian blur kernel. Deconvolution, on the other hand, is generally considered an [[ill-posed]] [[inverse problem]] that is best solved by nonlinear approaches. While unsharp masking increases the apparent sharpness of an image in ignorance of the manner in which the image was acquired, deconvolution increases the apparent sharpness of an image, but is based on information describing some of the likely origins of the distortions of the light path used in capturing the image; it may therefore sometimes be preferred, where the cost in preparation time and per-image computation time are offset by the increase in image clarity.

With deconvolution, "lost" image detail may be approximately recovered, although it generally is impossible to verify that any recovered detail is accurate. Statistically, some level of correspondence between the sharpened images and the actual scenes being imaged can be attained. If the scenes to be captured in the future are similar enough to validated image scenes, then one can assess the degree to which recovered detail may be accurate. The improvement to image quality is often attractive, since the same validation issues are present even for un-enhanced images.

For deconvolution to be effective, all variables in the image scene and capturing device need to be modeled, including [[aperture]], [[focal length]], distance to subject, lens, and media [[Refractive index|refractive indices]] and geometries. Applying deconvolution successfully to general-purpose camera images is usually not feasible, because the geometries of the scene are not set. However, deconvolution is applied in reality to microscopy and astronomical imaging, where the value of gained sharpness is high, imaging devices and the relative subject positions are both well defined, and optimization of the imaging devices to improve sharpness physically would cost significantly more. In cases where a stable, well-defined aberration is present, such as the lens defect in early [[Hubble Space Telescope]] images, [[deconvolution]] is an especially effective technique.