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이웃 및 블록 처리

필터링과 I/O 연산을 위한 이웃 및 블록 정의

일부 영상 처리 연산에서는 전체 영상을 한 번에 처리하지 않고 영상을 섹션으로 나눠 처리합니다. 슬라이딩 이웃 연산(Sliding Neighborhood Operation)은 각 픽셀 이웃에 알고리즘을 적용하여 영상을 한 번에 한 픽셀씩 처리합니다. 개별 블록 처리(Distinct Block Processing)에서는 영상을 겹치지 않는 동일한 크기의 블록으로 나누고 각 개별 블록에 알고리즘을 적용합니다. 그런 다음 이웃과 블록을 다시 조합하여 출력 영상을 구성합니다.


blockproc영상을 개별 블록 단위로 처리
bestblkDetermine optimal block size for block processing
nlfilterGeneral sliding-neighborhood operations
col2imRearrange matrix columns into blocks
colfiltColumnwise neighborhood operations
im2colRearrange image blocks into columns


ImageAdapterInterface for image I/O

도움말 항목

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.

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