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Image Deblurring

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

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Image Deblurring

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

  2. Abstract The goal of my project is to take an image input, artificially blur it using a known blur kernel, then using deconvolution 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. The final step is then to estimate the blur kernel of an image with an unknown blur kernel.

  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 • Convert image to frequency domain using the 2D Discrete Fourier Transform and the FFT. • Utilize the formula eθi= cosθ + isinθ • Usually display the magnitude, since DFT produces complex number (a + bi). Magnitude = (a2 + b2)1/2 • Scale to 0-255 range • O(n2)

  9. Current Work (Cont.) • Separate sums • 1D DFT in one direction (vertical/horizontal), then in the other • O(nlog2n)

  10. Current Work (Cont.) • Converting image back to spatial domain with Inverse Fourier Transform • Also possible to separate • Need full complex number from DFT or FFT Original Picture Magnitude Only Phase Only

  11. Future Work • First step: get FFT and IFFT to work in conjunction  convolution • Test with various types of blue kernels • Second step: reverse process and deconvolute • Noise Reduction as a follow up step • Blur kernel estimation

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