Neighborhood and Block Processing
Certain image processing operations involve processing an image in sections, rather than processing the entire image at once. A sliding neighborhood operation processes an image one pixel at a time, by applying an algorithm to each pixels neighborhood. In distinct block processing, an image is divided into equally-sized blocks without overlap, and the algorithm is applied to each distinct block. The neighborhoods and blocks are then reassembled to form the output image.
|Interface for image I/O|
Divide an image into sections, called blocks or neighborhoods, to reduce the memory needed to process the image.
A sliding neighborhood operation is performed one pixel at a time using information about the pixel’s neighborhood.
Distinct block processing divides an image into nonoverlapping rectangular sections that can be processed individually.
Using larger block sizes reduces overall computation time but requires more memory to process each block.
Reshape sliding neighborhoods and distinct blocks to reduce the execution time of processing an image.
To work with image data in file formats not supported by block processing functions, construct a class that manages files based on region.
If you have a Parallel Computing Toolbox™ license, you can take advantage of multiple processor cores on your
machine to improve the performance of