Recursive Soft Morphological Filters
IMAGE PROCESSING PROJECT
This semester project requires that you implement and test an image
filtering approach described in a given academic research conference
paper. The implementation is supposed to be performed in Matlab, but any
other programming language/ development environment of your choice is
acceptable. The project will be performed individually. The particular
project is assigned by random selection of one available paper from the
set of prepared papers.
The filter should be contained in a stand-alone function that uses Matlab
primitive functions only if that cannot be avoided. The function should be
documented and command line help must be provided. The function will
return a single image, having the same size as the input image. The
function must accept as input either an image, or the complete name/ path
of the image, or an interactive choice for user selection of image file.
The correct functioning of the implementation must be proven via
experiments: noise reduction for several different images, noise types and
noise intensities. The filter performance must be compared with the
performance of two classical filters: the mean filter and the median
filter (both implemented for a centered 3 x 3 filtering window). The
comparison must be subjective and objective (based on at least two image
quality measures, such as SNR, PSNR, MEA, etc...).
The experiments must be centralized within a written document. The
document must contain the following sections: filter description
(algorithm), implementation description, experiments, conclusions and
references.
This semester project requires that you implement and test an image
filtering approach described in a given academic research conference
paper. The implementation is supposed to be performed in Matlab, but any
other programming language/ development environment of your choice is
acceptable. The project will be performed individually. The particular
project is assigned by random selection of one available paper from the
set of prepared papers.
The filter should be contained in a stand-alone function that uses Matlab
primitive functions only if that cannot be avoided. The function should be
documented and command line help must be provided. The function will
return a single image, having the same size as the input image. The
function must accept as input either an image, or the complete name/ path
of the image, or an interactive choice for user selection of image file.
The correct functioning of the implementation must be proven via
experiments: noise reduction for several different images, noise types and
noise intensities. The filter performance must be compared with the
performance of two classical filters: the mean filter and the median
filter (both implemented for a centered 3 x 3 filtering window). The
comparison must be subjective and objective (based on at least two image
quality measures, such as SNR, PSNR, MEA, etc...).
The experiments must be centralized within a written document. The
document must contain the following sections: filter description
(algorithm), implementation description, experiments, conclusions and
references.
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