  Genetic Programming (GP) has been primarily used to tackle optimization,
classification, and feature selection related tasks. The widespread use of GP
is due to its flexible and comprehensible tree-type structure. Similarly,
research is also gaining momentum in the field of Image Processing, because of
its promising results over vast areas of applications ranging from medical
Image Processing to multispectral imaging. Image Processing is mainly involved
in applications such as computer vision, pattern recognition, image
compression, storage, and medical diagnostics. This universal nature of images
and their associated algorithm, i.e., complexities, gave an impetus to the
exploration of GP. GP has thus been used in different ways for Image Processing
since its inception. Many interesting GP techniques have been developed and
employed in the field of Image Processing, and consequently, we aim to provide
the research community an extensive view of these techniques. This survey thus
presents the diverse applications of GP in Image Processing and provides useful
resources for further research. Also, the comparison of different parameters
used in different applications of Image Processing is summarized in tabular
form. Moreover, analysis of the different parameters used in Image Processing
related tasks is carried-out to save the time needed in the future for
evaluating the parameters of GP. As more advancement is made in GP
methodologies, its success in solving complex tasks, not only in Image
Processing but also in other fields, may increase. Additionally, guidelines are
provided for applying GP in Image Processing related tasks, the pros and cons
of GP techniques are discussed, and some future directions are also set.
