How do deconvolution algorithms work?
There are basically three different deconvolution methods. All of them significantly reduce the out of-focus haze in microscope data sets. • Single image deconvolution • Deconvolution using 3 images (nearest neighbor) • Deconvolution using the whole volume (constrained iterative). Single Image deconvolution is used when the acquired image or images are not a volume scan. For example, researchers studying fast events must keep the image at the same focal plane and quickly capture an image. Each image is a snapshot of the specimen at a unique time sample. These images can be deconvolved by a single image deconvolution method to remove some of the out-of-focus haze. Nearest Neighbor deconvolution is useful when the specimen can be imaged along the optical axis and a series of images captured and stored to disk. The resulting data set is a volume representation of the object. The image can be deconvolved using the nearest neighbor method. In this approach, three consecutive images are used
