Day 18

  I pretty much finished my codes today.
  In the morning, I met Dr. Pelz and he suggested me write up a brief explanation about my code.
  In the afternoon, we went outside to take some videos about our eyes when wearing eye tracker. After that, I read something about basic machine learning.
  It sure is a busy day today!

outline

Outline

Slide1:  (title) Cosmic Ray Damaged Images Repair  (picture1)Damaged (picture2)After repairing

Slide2: (title) summary of the presentation

What is cosmic rays and how do they affect the images?

challenges we are facing

brief history about previous works

(*)The algorithm of removing the hot pixels

(*)How the algorithm works.

Possible applications

Slide3:  (title) How do Cosmic Rays Damage Images?

The images taken by us on the earth won’t be affected by cosmic rays because the atmosphere blocks most of the cosmic rays. However, in the space there are many kinds of cosmic rays existing. We can’t see cosmic rays directly, but their energy is high enough to cause damage on the space camera. The damage may cause the resulting images to have hot pixels on them.

Image of hot pixels.

Slide4: (title) Challenges

1.       Distinction between hot pixels and stars (image).

2.       Removing hot pixels from solar panels. (image)

Silde5: (title) previous work

Kevin Moser from RIT did an incredible work on this project. (with an Image)

Limitations(maybe)

Slide6: (title)algorithm—brief whole

….

Slide7: (title)algorithm sub-step1

…

Slide8: (title)algorithm sub-step2

…

Slide9: (title)algorithm sub-step3

…

Not sure how many slides will be used for sub-steps.

Slide 11:

(title)Comparison between the image before with the image after.

Two images

Some statistics. Chart true positives, true negatives, false positives, false negatives

Slide 12:

(title)Applications.

Make the images look better. It can also help to clean the noise so we can see the stars more clearly.

Day 17

  Dr. Pelz is not here today, but he gave me many suggestions yesterday.
  I tried his suggestion today and it worked pretty well. Although I'm not sure if it will work on all kinds of images, I decide to keep it as it works on the images I'm working on. I haven't started my outline yet, but I don't think it will take too much effort.
  In the afternoon at around 2pm, I felt sleepy again -- maybe because it's kind of warm in our room. I went for a walk and drank some water, and they worked out significantly.
  I think I'm almost done with the project, but I still have more things to do and I'm still looking for a more convenient way to achieve it.

Day16

  The most exciting day so far!
  In the morning, I wrote more code about my algorithm and read some paper about machine learning. After lunch, I went to the group meeting and "drove" a car on a simulator. It was the best game I've ever played in my life: real car, "real" traffic, real sound.
  After that, Dr. Pelz shared with me his way of removing hot pixels. Although it's more convenient and easy to code, his program met the same problem as mine -- they are not able to effectively remove the hot pixels on the solar panels. Nevertheless, Dr. Pelz gave me some really valuable suggestions and they can improve the algorithm very much.
  The observatory at night was overall satisfying. Although we were not able to actually observe any stars because of the cloudy sky, we had a wonderful meal and we learned a lot about both the history of the observatory and how an eclipse happens.
 

Day15

   I'm at half way through the internship.
   In the morning, we shared the projects we are doing. It seems that most of other interns had accomplished a lot on their projects. I tested different thresholds on the algorithm, and chose a suitable one and sent it to Dr. Pelz to see if it works. I also helped Jeremy with his Pupil Lab calibration.
   In the afternoon, I was trying to increase my algorithm's accuracy. I tested new method and I haven't finished coding yet. I helped Jeremy test both the Pupil Lab calibration and the Positive Science calibration.
   I forgot to take my lunch today. Instead, I had Pizza for lunch. How lucky!

Day14

  First thing of today is finding a way to read h5 files. After searching online, I found some libraries that support reading hdf5 files. However, they only seem to be friendly to Visual C++. While I was thinking if I should try to move to Visual C++, I found a software which is published by hdf5 company saying that it can read hdf5 files. After actually looking at it, I found it more powerful than it looked like: it can export hdf5 to text files. Although using C++ to read text files is not that fast, it's now the best way for me to at least read a raw image.

  After lunch, I started to write codes for it. After about half an hour of coding, my code gave me the result I wanted which was kind of out of my expectation. Then I was kind of wandering around and reading more tutorials. Then Dr. Pelz came in and we looked at the resulting image. Dr. Pelz seemed to be excited and he immediately asked me to perform Bayer filter on it. After doing some research, I performed Bayer filter on the images. I'm still a little afraid that the program is not able to effectively keep all the original features when removing most of the noises.

   I will try changing some of the constants tomorrow and see how the program works out.

Day 13

  This morning, Dr. Pelz met with us and he patiently and carefully helped me make the algorithm more clear so I can start implementing the algorithm on raw images.
  I don't know why C++ makes it so hard to read and write raw images. I spent so much time trying to figure out how to read a raw image. In the end, Dr. Pelz exported the raw image as a hdf5 file for me to read when I was about to learn Python. What I'm going to do is to modify my previous code that can only run on tiff images so it can directly run on NEF images. I'm going to have a lot of code to write I can't wait to see the result!