immse
Mean-squared error
Syntax
Description
Examples
Calculate Mean-Squared Error in Noisy Image
Read image and display it.
ref = imread('pout.tif');
imshow(ref)
Create another image by adding noise to a copy of the reference image.
A = imnoise(ref,'salt & pepper', 0.02);
imshow(A)
Calculate mean-squared error between the two images.
err = immse(A, ref);
fprintf('\n The mean-squared error is %0.4f\n', err);
The mean-squared error is 353.7631
Input Arguments
X
— Input array
numeric array
Input array, specified as a numeric array of any dimension.
Data Types: single
| double
| int8
| int16
| int32
| uint8
| uint16
| uint32
Y
— Input array
numeric array
Input array, specified as a numeric array of the same size and data type as
X
.
Data Types: single
| double
| int8
| int16
| int32
| uint8
| uint16
| uint32
Output Arguments
err
— Mean-squared error
positive number
Mean-squared error, returned as a positive number. The data type of err
is double
unless the input arguments are of data type
single
, in which case err
is of
data type single
Data Types: single
| double
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
immse
supports the generation of C
code (requires MATLAB®
Coder™). For more information, see Code Generation for Image Processing.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Version History
Introduced in R2014b
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