The input image must have a gray scale range greater than or equal to the range specified by max and min. Linear range rescaling merely propagates the coding inefficiencies. If the same relative contrast between tissue ranges is maintained by linear scaling to a lower gray scale resolution, the fine detail within each range is lost. The resulting image will have a statistical information content very near the target coding range or the original image entropy, whichever is less. Note that the mapping is non-linear, and may complicate analysis of absolute image gray scale value.
Spatial convolution forms the basis of the linear filters.
Because the convolution integral is itself a linear system, there in no loss of information in convolution with any PSF, and all convolutions are reversible. Convolving with a small PSF may be efficiently performed in the spatial domain, but convolution with a PSF of unknown size or a significant fraction of the image size is more efficiently performed in the Fourier domain. Deconvolution is generally performed only in the Fourier domain. The non linear spatial filters may be thought of as similar to convolution in that a small moving region of the image is considered for calculation of each output pixel value. Since the calculation or decision is non-linear, the operations generally result in a loss of information and are not reversible.