Shading Correction

Functions
I32	shading_correct_2D_mul (image *src, image *dst, image *mul)
	Perform Multiplicative Shading Correction for an Image (Normal Non-Linescan Type). More...
I32	shading_correct_2D_mul_calib (image *src, image *mul, U32 u32AvgSizePx)
	Gets Shading Correction Values from a Homogeneous Image. More...
I32	shading_correct_1D (image *src, image *dst, I8 *offs, U16 *mul)
	Perform 1D Shading Correction for an Image (Linescan Type). More...
I32	shading_correct_1D_calib (image *src1, image *src2, F32 t1, F32 t2, I8 *offs, U16 *shade)
	Calibrate the Shading Correction for an Image (Linescan Type). More...
I32	line_IIR (image *src, image *dst, I32 tol, I32 int_const, I32 p_const, I32 target)
	Perform Line IIR Regulation of Background Intesity (Linescan Type). More...

Detailed Description

Function Documentation

shading_correct_2D_mul()

I32 shading_correct_2D_mul	(	image *	src,
		image *	dst,
		image *	mul
	)

This function calculates the multiplicative shading correction for image src and outputs the result in image dst. The shading correction algorithm consist of a multiplication with the line shading buffer mul. The values in the shading buffer must be 256 for the identity operation. Larger values will result in an amplification, smaller values will result in a de-amplification. The formula is:

The minimum dimensions of all images will be processed.

Parameters

src	Source Image (Grey Image).
dst	Destination Image (Grey Image), may be identical with src.
mul	Multiplication Factor Image of Type IMAGE_GREY16.

Memory Consumption

None.

Return values

ERR_TYPE	if Input or Output Image is not a Grey Image, or Multiplicator Image is not of Type IMAGE_GREY16.
ERR_NONE	on Success.

shading_correct_2D_mul_calib()

I32 shading_correct_2D_mul_calib	(	image *	src,
		image *	mul,
		U32	u32AvgSizePx
	)

This function gets the values for the product image used for correcting multiplicative shading, like vignetting. After smoothing the source image over the given square pixel size, its mean value will be calculated. Then the values will be calculated according to the following formula:

Parameters

src	Source Image (Grey Image).
mul	Multiplication Factor Image of Type IMAGE_GREY16.
u32AvgSizePx	Smoothing Parameter for Noise Reduction.

Return values

ERR_TYPE	if src Image is not a Grey Image, or mul Image is not of Type IMAGE_GREY16.
ERR_SINGULAR	if src Image has a Grey Value of 255 or 0.
ERR_MEMORY	if Image Allocation Fails.
boxavg()	Error Code.
ERR_NONE	on Success.

shading_correct_1D()

I32 shading_correct_1D	(	image *	src,
		image *	dst,
		I8 *	offs,
		U16 *	mul
	)

This function calculates the shading correction for image src and outputs the result in image dst. The shading correction algorithm consist in a subtraction of the offset values stored in the line offset buffer offs and a multiplication with the line shading buffer mul. The offset values may be positive (offset will be subtracted) and negative (offset wil be added). The values in the shading table must be 256 for the identity operation. Larger values will result in an amplification, smaller values will result in a de-amplification.

The size of both images must be identical. The size of the buffers must be dx = src->dx.

Note

This function was named shading_correct() previously.

Parameters

src	Source Image (Grey Image).
dst	Destination Image (Grey Image).
offs	Line Offset Buffer.
mul	Line Shading Buffer.

Memory Consumption

None.

Return values

ERR_TYPE	if Input or Output Image is not a Grey Image.
ERR_NONE	on Success.

shading_correct_1D_calib()

I32 shading_correct_1D_calib	(	image *	src1,
		image *	src2,
		float	t1,
		float	t2,
		I8 *	offs,
		U16 *	shade
	)

This function performs the line calibration necessary for the shading correction. Images src1 and src2 must be images of e.g. a plain white surface taken at different shutter speeds t1 (for src1) and t2 (for src2). The function the calculates the offset and shading tables.

Care must be taken that the source images are not overexposed and have grey values in a reasonable range. If the camera features positive pixel offset, it is possible to have one image taken at a very short exposure time with very low grey values. In this case this image would mainly be responsible for the calculation of the offset table offs, while the other image would mainly be responsible for the shading gain table shade. It is clear that there must be some difference in shutter time (hence in grey values), otherwise the algorithm will not produce stable results.

The routine performs a vertical projection on both of the input images. The images are then compared and the offset and shading tables are calculated so that for the maximum contrast value the multiplication (shade) will be 256 (which is aquivalent to a multiplication with 1.0).

The size of both images must be identical. The size of the buffers must be dx = src->dx.

Parameters

src1	Source Image 1 (Grey Image).
src2	Source Image 2 (Grey Image).
t1	Shutter Value for image 1.
t2	Shutter Value for image 2.
offs	Line Offset Table (Output).
shade	Shading Table (Output).

Note

This function was named line_calibrate() previously.

Memory Consumption

None.

Return values

ERR_TYPE	if src1 or src2 is not Grey Image.
ERR_MEMORY	if Memory Allocation Fails.
ERR_NONE	on Success.

line_IIR()

I32 line_IIR	(	image *	src,
		image *	dst,
		I32	tol,
		I32	int_const,
		I32	p_const,
		I32	target
	)

This function performs a recursive filter used in linescan applications.

Parameters

src	Source Image (Grey Image).
dst	Destination Image (Grey Image).
tol	Grey Value Tolerance for Averaging.
int_const	Integration Parameter.
p_const	Proportional Parameter.
target	Target Grey Value.

The algorithm may be used for surface inspection, where a homogeneous surface is inspected. It is mainly a regulation (tracking) of the grey values in a line. Assume, that the grey values in a line are in the same range within a tolerance (after the shading correction). Pixels not within this tolerance (tol) are not considered. All the pixels within the tolerance are subtracted from the tracking average and the difference is integrated. The difference itself (proportional regulation) and the integrated difference (integration regulation) are multiplied with the corresponding constant int_const/1024 and p_const/1024 and used to produce a new tracking average. This average is then subtracted for the complete line, the target value target is added to produce a destination image with an average value of target.

The size of both images must be identical. The size of the buffers must be dx = src->dx.

Return values

ERR_TYPE	if Input or Output Image is not a Grey Image.
ERR_NONE	on Success.