1. Vincent DeVito Computer Systems Lab 2009-2010
Image Deblurring
Vincent DeVito
Computer Systems Lab

2. Abstract
The goal of my project is to take an image input, artificially blur it using a known blur kernel, then using deconvolution techniques to deblur and restore the image, then run a last step to reduce the noise of the image. The goal is to have the input and output images be identical with a blurry intermediate image.

3. Abstract Condensed

4. Background Running goal for image processors and photo editors
Many methods of deconvolution exist
Many utilize the Fourier Transform
Current progress focused on blur kernel estimation
Better kernel  more accurate, clear output image

5. Related Projects
The group of Lu Yuan, et al. designed project with blurry/noisy image pairs
Blurry image intensity + noisy image sharpness + deconvolution = sharp, deblurred output image
The group of Rob Fergus, et al. designed project to estimate blur kernel from naturally blurred image
A few inputs + kernel estimation algorithm + deconvolution = deblurred output image with few artifacts

6. Application
Photography
Improve image quality
Restore image

7. Application (Cont.) Machine Vision
Requires input images to be of good clarity
Blur could ruin techniques such as edge detection
Intermediate step

8. Current Work
Basic image processing techniques (HIPR2 online worksheets)
Pointwise operations, geometric operations, morphology

9. Expected Results First version
Clear input  artificial blurring using known blur kernel  deconvolution techniques using same kernel  reduce noise  output image
Hope to have the output image be as clear and sharp as the original input image

10. Expected Results (Cont.)
Final Version (hopefully)
Naturally blurred input  estimation of unknown blur kernel  deconvolution techniques using that kernel  reduce noise  output image
Hope to have the output image be a clear, sharp version of the blurry input image