Non-uniformity correction of infrared images by midway equalization
Functions
MIRE.h File Reference
#include <vector>
Include dependency graph for MIRE.h:

Go to the source code of this file.

Functions

float * MIRE (float[], float, int, int)
void MIRE_automatic (float[], int, int, int, int, float)
 Arguments : image, std-dev of the gaussian, image size.
float TV_column_norm (float[], int, int, float)
 Arguments : image, image size, sigma_min,simga_max,sigma_step:
void specify_column (float[], int, int, int, std::vector< float >)
 Arguments : image, image size.
float gaussian (int, float)
 Arguements : imge, image size, column to be processed, target values.
std::vector< std::vector< float > > target_histogram (std::vector< std::vector< float > >, int, int, float)
 Arguements : position (in pixel), std-dev.
std::vector< std::vector< float > > column_sorting (float[], int, int)
 Arguments : vector of vector containing the sorted values, image size,sigma.
std::vector< float > histo_column (float[], int, int, int)
 Arguments : image, image size.

Function Documentation

std::vector<std::vector<float> > column_sorting ( float  [],
int  ,
int   
)

Arguments : vector of vector containing the sorted values, image size,sigma.

float gaussian ( int  x,
float  sigma 
)

Arguements : imge, image size, column to be processed, target values.

Arguements : imge, image size, column to be processed, target values.

Parameters:
xpoint of evaluation
sigmastd-dev of the gaussian
Returns:
1/(sigma*sqrt(2*M_PI))*exp(-(x*x)/(2*sigma*sigma))

Definition at line 219 of file MIRE.cpp.

Here is the caller graph for this function:

std::vector<float> histo_column ( float  [],
int  ,
int  ,
int   
)

Arguments : image, image size.

float* MIRE ( float  [],
float  ,
int  ,
int   
)
void MIRE_automatic ( float  [],
int  ,
int  ,
int  ,
int  ,
float   
)

Arguments : image, std-dev of the gaussian, image size.

void specify_column ( float  [],
int  ,
int  ,
int  ,
std::vector< float >   
)

Arguments : image, image size.

std::vector<std::vector<float> > target_histogram ( std::vector< std::vector< float > >  V_HISTOS,
int  w1,
int  h1,
float  sigma 
)

Arguements : position (in pixel), std-dev.

Arguements : position (in pixel), std-dev.

Parameters:
V_HISTOSvector containing sorted columns.
w1image width
h1image height
sigmastd-dev of the Gaussian.
Returns:
FINAL

Compute the midway Gaussian averaged histogram. Gaussian weighted, troncated with radius equal to 4 sigma: only the columns in the interval ["column"-4sigma, "column"+4sigma] are taken into account. Input: image, column current, std-dev sigma Output : Midway-gaussian-averaged histogram (vector).

Implemented in 3 steps: Step1 : extract columns columns in the interval ["column"-4sigma, "column"+4sigma] Step2 : Sort all vectors obtained from step1 separately Step3 : for all lines and all column of step2 compute the midway gaussian averaged ie : v(i)=WEIGHT(column).*(paquet(ligne,column)));

Definition at line 316 of file MIRE.cpp.

Here is the call graph for this function:

Here is the caller graph for this function:

float TV_column_norm ( float  [],
int  ,
int  ,
float   
)

Arguments : image, image size, sigma_min,simga_max,sigma_step:

 All Files Functions Defines