Single Pixel Camera Project 2016 @ UoL: 三月 2016

Last week, week 4, our group processed the large image with divide the image into small blocks. The block size was 160*160 and we compressed the image to let it have the same length and width, which are the actual times of 160. We found that the process speed is very low and it takes us approximately 27 hours to deal with a 1920*1200 image. And the result returns with black image. Even we tried to process one small blcok of the image, it returns a black block, either.

In this week, our group works on the following aspects:

1. Optimize the code in dividing image, after the reconstruction proecess, the program will not return a black block.

2. Our group created a GUI program to implement the process of the image.

3. Design our poster.

First Part

For the first part, our group tried to divide the large image into very small parts, for example, the 1010 block or 2020 block. Our group found some small conslusions on the following:

1. In this project, the process is mainly about matrix calculation. For the 160*160 block, the program needs to generates one matrix, which has the (number of masks)*(the square of block length). The complexcity of the program is O(n^2). n is the length of the small block. When the length of the squrare decreased from 160 to 20, the order of magnitude decreased approximately 1. Then the speed decreased about 2 order of mangnitude. The process speed for one block image increased a lot.

2. Assume the time consumed is t, then t = n*s. In this equation, n is the number of the blocks, the s is the time consumed in processing one block. The s decreased for n is bigger. For example, when the block length decreased from 160 to 20, the n increased about one order of magnitude, but s decreased about 2 order of mangnitude. This shows that the speed will not increase as the the block numbers become more, when n reache one approximately value, the time consumed will becomes the maximum. According to our timer, the time when the block length is equal to 10, the time cosumed is slightly more then when the block length is 20.

For the 2 aspects, our group summarizes the conslusion roughly and illustrate the relationship between the time consumed and the number of blocks for one fixed graph (1920*1200).

Relationship between Time Consumed and Block Length

3. Here we defined a parameter r, which is number of masks N over the square of blcok length l. r = N/(l^2). r is in the interval (0,1]. For r approches 1 more, the quality of the reconstruction is better. Which means they have higher similarity.

The conclusion between the rate and the similarity is that if the rate increased, the similarity is higher for there are more masks pattern and ensure the better combine quality.