Course Assessment
This is an autogradable course assessment (CA) for the ECMM426 Computer Vision module, which represents 60% of the overall module assessment.
This is an individual exercise and your attention is drawn to the College and University guidelines on collaboration and plagiarism, which are available from the University of Exeter
website
(https://www.exeter.ac.uk/students/administration/complaintsandappeals/academicmisconduct/)
.
Important:
1. Do not change the name of this notebook and the containing folder. The notebook and the folder shouldrespectively be named as CA.ipynb
and
CA
.
2. Do not add and remove/delete any cell. You can work on a draft notebook and only copy the
functions/implementations here.
3. Do not add your name or student code in the notebook or in the file name.
4. Each question asks for one or more functions to be implemented.
5. Each question is associated with appropriate marks and clearly specifies the marking criteria. Most of the questions have partial grading.
6. Each question specifies a particular type of inputs and outputs which you should regard.
7. Each question specifies data for your experimentation and test which you can consider.
8. A hidden unit test is going to evaluate if all the desired properties of the required function(s) are met or not.
9. If the test passes all the associated marks will be rewarded, if it fails 0 marks will be awarded.
10. There is no restriction on the usage of any function from the packages from pip3 distribution.
11. While uploading your work on e-Bart, please do not upload the EXCV10 and MaskedFace datasets you use for training your model.
Question 1 (3 marks)
Write a function
add_gaussian_noise(im, m, std)
which will add Gaussian noise with mean
m
and
standard deviation
std
to the input image
im
and will return the noisy image. Note that the output image must be of uint8
type and the pixel values should be normalized in .
Inputs
im
is a 3 dimensional numpy array of type
uint8
with values in .
m is a real number.
std
is a real number.
Outputs
The expected output is a 3 dimensional numpy array of type
uint8
with values in .
Data
You can work with the image at
data/books.jpg
.
Marking Criteria
The output with a particular
m
and
std
should exactly match with the correct noisy image with that
m and std
to obtain the full marks. There is no partial marking for this question.
In?[?]:
# Gaussian noise
def
add_gaussian_noise
(im, m, std):
# YOUR CODE HERE
raise
NotImplementedError()
In?[?]:
# This cell is reserved for the unit tests. Please leave this cell as it is.
In?[?]:
# This cell is reserved for the unit tests. Please leave this cell as it is.
Question 2 (3 marks)
Speckle noise is defined as multiplicative noise, having a granular pattern, it is the inherent property of Synthetic Aperture Radar (SAR) imagery. More details on Speckle noise can be found
here (https://en.wikipedia.org/wiki/Speckle_(interference))
. Write a function
add_speckle_noise(im, m, std) which will add Speckle noise with mean m
and standard deviation
std
to the input image
im
and will return the noisy image. Note that the output image must be of uint8
type and the pixel values should be normalized in [0, 255] .
Inputs
im
is a 3 dimensional numpy array of type
uint8 with values in [0, 255]
m
is a real number.
std
is a real number.
Outputs
The expected output is a 3 dimensional numpy array of type
uint8 with values in [0, 255] .
Data
You can work with the image at
data/books.jpg
.
Marking Criteria
The output with a particular
m
and
std
should exactly match with the correct noisy image with that
m and std
to obtain the full marks. There is no partial marking for this question.
In?[?]:
# Speckle noise
def
add_speckle_noise
(im, m, std):
# YOUR CODE HERE
raise
NotImplementedError()