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Advanced Image Deblurring Techniques Using Deconvolution and Blur Kernel Estimation

This project focuses on restoring blurred images through a systematic approach involving artificial blur application, deconvolution, and noise reduction. Starting with an input image and a known blur kernel, the work aims to generate an identical output image despite the intermediate blurriness. Key challenges addressed include blur kernel estimation from unknown blurred images and the implementation of effective deconvolution algorithms. Current advancements include successful implementations of FFT, IFFT, and convolutional functions, paving the way for more accurate image restoration in applications such as photography and machine vision.

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Advanced Image Deblurring Techniques Using Deconvolution and Blur Kernel Estimation

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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. 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

  4. 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

  5. Application • Photography • Improve image quality • Restore image From Fergus, et al.

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

  7. Fourier Transform • Extremely useful for convolution and deconvolution • Convert image to frequency domain • 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)

  8. Fourier Transform (Cont.) • Separate sums • 1D DFT in one direction (vertical/horizontal), then in the other • O(nlog2n)

  9. Fourier Transform (Cont.) • Inverse Fourier Transform converts back to spatial domain • Also possible to separate • Need full complex number from DFT or FFT Original Picture Magnitude Only Phase Only

  10. Current Work • Successful FFT and IFFT program • Successful convolution program • Takes any image (square image of size 128x128 or smaller for best runtime) and blurs it using any given blur kernel

  11. Current Work (Cont.) • Start to image deconvolution using a given kernel • Inconsistent and somewhat noisy

  12. Future Work • Fix deconvolution algorithm • Inconsistent and produces large, clustered values • Need a new transform or more research into kernel types • Noise reduction • Research into deconvolution based on kernel type

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