Hi,
I'm proposing a Color Constancy filter as a project idea for GSoC and
was asking for any remarks about that idea.
Color constancy is the ability of the human visual system that
ensures the perception of the color of objects to remain relatively
constant under varying illumination conditions. A color constancy filter
would basically be applied on images with various degrees of damage to
restore original colors as close as possible.
This <https://imgur.com/a/hj2ox> is a simple example of a low-statistics
filter output I've implemented as an academic project along with the
original damaged image. The filter is an implementation of a
mathematical equation presented with some parameters that's used to
introduce variations to the filter.
Use cases: despite being an end-result filter that helps retrieving
damaged images original colors it's very helpful as a pre-processing
utility for many computer vision algorithms. A simple example would be a
classifier that identifies an object utilizing its color.
Implementation: this is still a research filed where last paper I've
read was out earlier this year and still more and more papers are being
published introducing various learning-methods to improve accuracy
including unsupervised and supervised methods and even new
learning-based algorithms to improve low-statistics light-computational
ones. Variations of this filter and their accuracies will also prove to
be a very appropriate project for GSoC since there is many factors
affecting the choice of filter such as real-time need, filed of
application, available dataset, ..etc. So, it's more like a group of
filters.
Impact: introduction of machine learning to this filed is quiet
recent so enhancing FFmpeg with this technology and possibly adding to
it will prove entrepreneurship. Photography, Computer Vision and many
other fields that performs fine image processing would make good use of
this filter.
Best regards,
Mina Sami.
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