Nonlinear filtering using lookup tables

performs a 2-by-2 or 3-by-3 nonlinear neighborhood filtering operation on binary or
grayscale image `J`

= bwlookup(`BW`

,`lut`

)`I`

and returns the results in output image
`J`

. The neighborhood processing determines an integer index value used
to access values in lookup table `lut`

. The fetched
`lut`

value becomes the pixel value in output image
`J`

at the targeted position.

You optionally can perform the filtering using a GPU (requires Parallel Computing Toolbox™).

Construct the vector `lut`

such that the filtering operation places a `1`

at the targeted pixel location in the input image only when all four pixels in the 2-by-2 neighborhood of BW are set to `1`

.

lutfun = @(x)(sum(x(:))==4); lut = makelut(lutfun,2)

`lut = `*16×1*
0
0
0
0
0
0
0
0
0
0
⋮

Load a binary image.

`BW1 = imread('text.png');`

Perform 2-by-2 neighborhood processing with 16-element vector `lut`

.

BW2 = bwlookup(BW1,lut);

Show zoomed before and after images.

figure; h1 = subplot(1,2,1); imshow(BW1), axis off; title('Original Image') h2 = subplot(1,2,2); imshow(BW2); axis off; title('Eroded Image') % 16X zoom to see effects of erosion on text set(h1,'Ylim',[1 64],'Xlim',[1 64]); set(h2,'Ylim',[1 64],'Xlim',[1 64]);

`BW`

— Input imagebinary image

Input image transformed by nonlinear neighborhood filtering operation, specified as
a binary image. For numeric input,
any nonzero pixels are considered to be `1`

(`true`

).

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

`lut`

— Lookup table of output pixel values 16-element vector | 512-element vector

Lookup table of output pixel values, specified as a 16- or 512-element
vector. The size of `lut`

determines which of the
two neighborhood operations is performed.

If

`lut`

contains 16 data elements, then the neighborhood matrix is 2-by-2.If

`lut`

contains 512 data elements, then the neighborhood matrix is 3-by-3.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

`J`

— Output imagebinary image | grayscale image

Output image, returned as a grayscale or binary image whose distribution of pixel
values are determined by the content of the lookup table, `lut`

. The
output image `J`

is the same size as the input image
`I`

and the same data type as `lut`

.

The first step in each iteration of the filtering operation performed by
`bwlookup`

entails computing the `index`

into vector
`lut`

based on the binary pixel pattern of the neighborhood matrix on
image `I`

. The value in `lut`

accessed at
`index`

, `lut(index)`

, is inserted into output image
`J`

at the targeted pixel location. This results in image
`J`

being the same data type as vector `lut`

.

Since there is a 1-to-1 correspondence in targeted pixel locations, image
`J`

is the same size as image `I`

. If the targeted
pixel location is on an edge of image `I`

and if any part of the 2-by-2 or
3-by-3 neighborhood matrix extends beyond the image edge, then these non-image locations are
padded with 0 in order to perform the filtering operation.

The following figures show the mapping from binary 0 and 1 patterns
in the neighborhood matrices to its binary representation. Adding
1 to the binary representation yields `index`

which
is used to access `lut`

.

For 2-by-2 neighborhoods, `length(lut)`

is 16. There are four pixels in
each neighborhood, and two possible states for each pixel, so the total number of
permutations is 2^{4} = 16.

To illustrate, this example shows how the pixel pattern in a 2-by-2 matrix determines
which entry in `lut`

is placed in the targeted pixel location.

Create random 16-element

`lut`

vector containing`uint8`

data.scurr = rng; % save current random number generator seed state rng('default') % always generate same set of random numbers lut = uint8( round( 255*rand(16,1) ) ) % generate lut rng(scurr); % restore

lut = 208 231 32 233 161 25 71 139 244 246 40 248 244 124 204 36

Create a 2-by-2 image and assume for this example that the targeted pixel location is location

`I(1,1)`

.I = [1 0; 0 1]

I = 1 0 0 1

By referring to the color coded mapping figure above, the binary representation for this 2-by-2 neighborhood can be computed as shown in the code snippet below. The logical 1 at

`I(1,1)`

corresponds to blue in the figure which maps to the Least Significant Bit (LSB) at position 0 in the 4-bit binary representation (,2^{0}= 1). The logical 1 at`I(2,2)`

is red which maps to the Most Significant Bit (MSB) at position 3 in the 4-bit binary representation (2^{3}= 8) .% I(1,1): blue square; sets bit position 0 on right % I(2,2): red square; sets bit position 3 on left binNot = '1 0 0 1'; % binary representation of 2x2 neighborhood matrix X = bin2dec( binNot ); % convert from binary to decimal index = X + 1 % add 1 to compute index value for uint8 vector lut A11 = lut(index) % value at A(1,1)

index = 10 A11 = 246

The above calculation predicts that output image A should contain the value 246 at targeted position

`A(1,1)`

.`A = bwlookup(I,lut) % perform filtering`

A = 246 32 161 231

`A(1,1)`

does in fact equal 246.

For 3-by-3 neighborhoods, `length(lut)`

is 512. There are nine pixels
in each neighborhood, and two possible states for each pixel, so the total number of
permutations is 2^{9} = 512.

The process for computing the binary representation of 3-by-3 neighborhood processing is the same as for 2-by-2 neighborhoods.

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

`bwlookup`

supports the generation of C code (requires MATLAB^{®}Coder™). Note that if you choose the generic`MATLAB Host Computer`

target platform,`bwlookup`

generates code that uses a precompiled, platform-specific shared library. Use of a shared library preserves performance optimizations but limits the target platforms for which code can be generated. For more information, see Code Generation Using a Shared Library.When generating code, specify an input image of class

`logical`

.

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

When generating code, specify an input image of class

`logical`

.

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Image Processing on a GPU.

A modified version of this example exists on your system. Do you want to open this version instead? (ko_KR)

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