Neighborhood and Block Processing

Define neighborhoods and blocks for filtering and I/O operations

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.

Functions

`blockproc`	Distinct block processing for image
`bestblk`	Determine optimal block size for block processing
`nlfilter`	General sliding-neighborhood operations
`col2im`	Rearrange matrix columns into blocks
`colfilt`	Column-wise neighborhood operations
`im2col`	Rearrange image blocks into columns
`blockedImage`	Big or multiresolution image made from discrete blocks (Since R2021a)
`apply`	Process blocks of blocked image (Since R2021a)

Topics

Neighborhood or Block Processing: An Overview
Divide an image into sections, called blocks or neighborhoods, to reduce the memory needed to process the image.
Sliding Neighborhood Operations
A sliding neighborhood operation is performed one pixel at a time using information about the pixel’s neighborhood.
Distinct Block Processing
Distinct block processing divides an image into nonoverlapping rectangular sections that can be processed individually.
Block Size and Performance
Using larger block sizes reduces overall computation time but requires more memory to process each block.
Use Column-wise Processing to Speed Up Sliding Neighborhood or Distinct Block Operations
Reshape sliding neighborhoods and distinct blocks to reduce the execution time of processing an image.
Perform Block Processing on Image Files in Unsupported Formats
To work with image data in file formats not supported by block processing functions, construct a class that manages files based on region.
Parallel Block Processing on Large Image Files
If you have a Parallel Computing Toolbox™ license, you can take advantage of multiple processor cores on your machine to improve the performance of blockproc.

Featured Examples

Block Processing Large Images

Perform edge detection on a large image by reading, processing, and writing the results back to disk, one block at a time.

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Compute Statistics for Large Images

Compute statistics from a large image, and then use that information to more accurately process the image blockwise.

Open Live Script