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Effective Techniques for Removing Motion Blur from Single Images

Motion blur can significantly degrade image quality, often resulting from object motion, camera translation, or rotation. This guide explores the Point Spread Function (PSF), convolution models, and de-blurring techniques to recover sharp images. We assume pure translation of the camera, no object motion during exposure, and minimal parallax for the ideal scenario. Experimental validation using DSLR hand-held shots demonstrates the application of these techniques, including the use of known kernels for effective deconvolution. Regularization methods are discussed to manage noise and stabilize results.

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Effective Techniques for Removing Motion Blur from Single Images

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  1. Removing motion blur from a single image

  2. Sources of blur • Object motion

  3. Sources of blur • Object motion • Translation of camera

  4. Sources of blur • Object motion • Translation of camera • Rotation of camera

  5. Sources of blur • Object motion • Translation of camera • Rotation of camera • Defocus • Internal camera distortions

  6. PSF = Point Spread Function (PSF) Assume: • Point light source

  7. B = KL + N Convolution model motivation • Assume: • No image plane rotation • No object motion during the exposure • No significant parallax (depth variation) Camera motion is Pure Translation!!! • Violation of assumption:

  8. B = KL + N Convolution model motivation • Assume: • No image plane rotation • No object motion during the exposure • No significant parallax (depth variation) Camera motion is Pure Translation!!! • Experimental validation: 8 subjects handholding DSLR with 1 sec exposure Close-up of dots

  9. Generation rule:B = KL + N Convolution Model • Notations • L: original image • K: the blur kernel (PSF) • N: sensor noise (white) • B: input blurred image  +

  10. Fourier Convolution Theorem! How can the image be recovered? Assumptions: • Known kernel (PSF) • Constant kernelfor the whole image • No noise Goal: • Recover Ls.t.: B = KL

  11. De-blur using Convolution Theorem Convolution Theorem:

  12. Recovered Example: Blurred Image PSF

  13. Deconvolution is unstable Example: Noisy case:

  14. FT of original signal Original signal Convolved signals w/w noise FT of convolved signals Reconstructed FT of the signal 1D example: Regularization is required

  15. 51 151 191 Regularizing by window Window size:

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