One of the commonly used sharpening methods in digital image-processing is unsharp masking. This method detects the edges within an image and increases them – rendering an effect of perceived sharpness. Deconvolution technology, on the other hand, tackles sharpness and image blur in a more complex but beneficial way.

In image processing, deconvolution is a restoration process that essentially allows a blurred image to be re-focused. It is able to achieve this by detecting the state of the image before the distortion occurred and approximately reversing the process that caused the optical blur to occur. While unsharp masking increases the perceived sharpness of an image, deconvolution increases the actual sharpness based on information which describes some of the likely origins of the distortions when capturing the image. With deconvolution, lost image detail may be approximately recovered.

Topaz InFocus uses this same deconvolution technology to mathematically reverse image blur while increasing the actual sharpness. InFocus can enhance the clarity of an already well-focused as well as deblur an out-of-focus or motion blurred image. The ability to re-focus a blurred image (after the fact) is a challenging but valuable capability. This technology can be applied to both enhancing the clarity in an already well-focused image, as well as de-blurring out-of-focus or motion blurred images.

A common side effect of the deconvolution process is deconvolution artifacts. These artifacts can appear as a collection of artifacts around lines of details in an image. Topaz InFocus includes a parameter specially designed to eliminate these artifacts.

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