Images can be distorted by blur, such as motion blur or blur resulting from an out-of-focus lens. Blur is represented by a distortion operator, also called the point spread function (PSF). Different deblurring algorithms estimate and remove blur based on how much knowledge you have of the PSF and noise in the image.
|Deblur image using blind deconvolution|
|Deblur image using Lucy-Richardson method|
|Deblur image using regularized filter|
|Deblur image using Wiener filter|
|Taper discontinuities along image edges|
|Convert optical transfer function to point-spread function|
|Convert point-spread function to optical transfer function|
Deblurring is a process that removes distortion from a blurry image, using knowledge of how the optical system blurs a single point of light.
The Lucy-Richardson deconvolution function enables you to deblur images with complicated distortions such as nonuniform image quality or undersampling.
Although blind deconvolution algorithm does not require information about the blurring or noise, it enables you to deblur images that have complicated distortions such as nonuniform image quality or undersampling.
To create your own deblurring functions, convert the distortion operator between the spatial and the frequency domains.
Ringing is an artifact that appears as alternating bright and dark bands near edges. Reduce ringing by blurring the edges of the original image.