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The Guile-CV procedures and methods to filter images.

`im-gaussian-blur`

`im-gaussian-blur-channel`

`im-gaussian-gradient`

`im-gaussian-gradient-channel`

`im-gaussian-sharp`

`im-gaussian-sharp-channel`

`im-sharpen`

`im-sharpen-channel`

`im-median-filter`

`im-median-filter-channel`

`im-convolve`

`im-convolve-channel`

`im-nl-means`

`im-nl-means-channel`

- Procedure:
**im-gaussian-blur***image sigma* - Procedure:
**im-gaussian-blur-channel***channel width height sigma* -
Returns a new image or channel.

The new image or new channel is the result of the computation of the Gaussian blurring, also known as the Gaussian smoothing, by means of a convolution of

`image`or`channel`with a 2D Gaussian function, where`sigma`is the standard deviation of the Gaussian distribution.

- Procedure:
**im-gaussian-gradient***image sigma* - Procedure:
**im-gaussian-gradient-channel***channel width height sigma* -
Returns a new image or channel.

The new image or new channel is the result of the computation of the strength of the first order partial derivatives by means of a convolution of

`image`or`channel`with the first order derivative of a 2D Gaussian function, where`sigma`is the standard deviation of the Gaussian distribution.

- Procedure:
**im-gaussian-sharp***image factor scale* - Procedure:
**im-gaussian-sharp-channel***channel width height factor scale* -
Returns a new image or channel.

The new image or new channel is the result of the computation of the Gaussian sharpening: the procedure does (a) perform a Gaussian smoothing at the given

`scale`to create a temporary image`smooth`

and (b) blends`image`and`smooth`

according to the formula`(- (* (+ factor 1) image) (* smooth factor))`

.

- Procedure:
**im-sharpen***image factor* - Procedure:
**im-sharpen-channel***channel width height factor* -
Returns a new image or channel.

This procedure performs a ‘

`simple sharpening`’ operation on`image`. It actually calls im-convolve with the following kernel:-1/16 -1/8 -1/16 0 0 0 ( * factor -1/8 3/4 -1/8 ) + 0 1 0 -1/16 -1/8 -1/16 0 0 0

and uses

`mirror`

as the ‘`out of bound strategy`’.

- Procedure:
**im-median-filter***image w-width w-height [#:obs 'repeat]* - Procedure:
**im-median-filter-channel***channel width height w-width w-height [#:obs 'repeat]* -
Returns a new image or channel.

In the new image or channel, each pixel value is the ‘

`median`’ value of neighboring entries. The pattern of neighbors is called a ‘`window`’, the size of which is given by`w-width`

and`w-height`

(see Median Filter for more information). Both`w-width`and`w-height`must be`odd`

numbers, inferior to`width`and`height`respectively.The optional keyword argument

`#:obs`determines the ‘`out-of-bound strategy`’. Valid`#:obs`symbols are:`avoid`

do not operate on pixels upon which (centering) the kernel does not fit in the image

`repeat`

repeat the nearest pixels

`mirror`

mirror the nearest pixels

`wrap`

wrap image around (periodic boundary conditions)

`zero`

out-of-bound pixel values to be

`0.0`

- Procedure:
**im-convolve***image kernel [#:obs 'repeat]* - Procedure:
**im-convolve-channel***channel width height kernel k-width k-height [#:obs 'repeat]* -
Returns a new image or channel.

The new image or new channel is the result of the convolution of

`image`using`kernel`. The kernel`k-width`and`k-height`values can be different, but they must be`odd`

numbers, inferior to`width`and`height`respectively.The optional keyword argument

`#:obs`determines the ‘`out-of-bound strategy`’. Valid`#:obs`symbols are:`avoid`

do not operate on pixels upon which (centering) the kernel does not fit in the image

`clip`

clip the kernel when operating on pixels upon which (centering) the kernel does not fit in the image (this is only useful if the kernel is >= 0 everywhere)

`repeat`

repeat the nearest pixels

`mirror`

mirror the nearest pixels

`wrap`

wrap image around (periodic boundary conditions)

`zero`

out-of-bound pixel values to be

`0.0`

Kernel data structure, accessors, procedures and predefined kernels are all described in this node of the Guile-CV manual: Kernel Structure and Accessors.

- Procedure:
**im-nl-means***image arg...* - Procedure:
**im-nl-means-channel***channel width height arg...* -
Returns a new image or channel.

The new image or new channel is the result of a non-local means denoising as described here

^{17}. The following table lists the optional keyword arguments and their default values:Policy arguments:

`#:policy-type 1`accepts 0 (ratio policy) or 1 (norm policy)

`#:sigma 15.0`default to 5.0 if the policy-type is 0

`#:mean-ratio 5.0`default to 0.95 if the policy-type is 0

`#:variance-ratio 0.5``#:epsilon 1.0e-5`

Filter arguments:

`#:spatial-sigma 2.0``#:search-radius 3``#:patch-radius 1`the patch-radius can be either 1 or 2

`#:mean-sigma 1.0``#:step-size 2``#:n-iteration 1`

The

`im-nl-means-channel`

procedure accepts one additional optional keyword argument:`#:n-thread (- (current-processor-count) 1)`

FIXME need to describe the parameters

P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot. An Optimized Blockwise Non Local Means Denoising Filter for 3D Magnetic Resonance Images . IEEE Transactions on Medical Imaging, 27(4):425-441, Avril 2008.

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