<< Chapter < Page Chapter >> Page >

Summary of approach

Our approach includes three parts: 1. generation of blurred images, 2. deblur, 3. evaluation.

general idea flow chart.

To generate the blurred images, we first convolve the impulse response of the boxcar and coded filter with the original image. Then, we addzero mean Gaussian noise to the blurred images to simulate the thermal and readout noise caused by real-world camera sensors.

We implemented the deblurring process for both the traditional shutter (boxcar filter) and the flutter shutter (coded filter) in both time andfrequency domains. In time domain implementation, we use least square estimation to solve the linear equation B = A X + n for X . In the above equation, B is the blurred image, A is the smear matrix corresponding to the filter type, and n is noise. In the frequencydom ain implementation, we divide the DFT of the blurred image by the DFT of the filter response, and take the inverse DFT to get our deblurred image.

After getting the deblurred images, we calculated the PSNR (explained in Results Section) to characterize how well each approach works. We plotted the PSNR with respect to the variance of the Gaussian noise we added to the blurred image to determine thedeblurring performance of the traditional shutter (boxcar filter) and the flutter shutter (coded filter).

Get Jobilize Job Search Mobile App in your pocket Now!

Get it on Google Play Download on the App Store Now




Source:  OpenStax, Elec 301 projects fall 2015. OpenStax CNX. Jan 04, 2016 Download for free at https://legacy.cnx.org/content/col11950/1.1
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Elec 301 projects fall 2015' conversation and receive update notifications?

Ask