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.

함수

`blockproc`	영상을 개별 블록 단위로 처리
`bestblk`	Determine optimal block size for block processing
`nlfilter`	일반적인 슬라이딩 이웃 연산
`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 (R2021a 이후)
`apply`	Process blocks of blocked image (R2021a 이후)

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.

이웃 및 블록 처리

함수

도움말 항목

추천 예제

Block Processing Large Images

Compute Statistics for Large Images