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Image Restoration Techniques Using Noise Functions: A Comprehensive Project Proposal

This project aims to design software that restores degraded images through the implementation of various noise functions. We will simulate atmospheric turbulence, motion blur, and salt-and-pepper noise to degrade original images. Next, we will develop restoration techniques using adaptive median filtering, inverse filtering, and sinusoidal noise functions to recover the images to their original quality. We aim to deepen our understanding of signal processing, the Fast Fourier Transform, and the application of various filters in specific noise contexts, leading to a practical experience in image restoration.

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Image Restoration Techniques Using Noise Functions: A Comprehensive Project Proposal

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  1. ECE 533 Project Tribute By: Justin Shepard & Jesse Fremstad

  2. Project Proposal: • Design software for restoring images. • Implement noise functions to degrade images • Then, implement the functions to restore the image to its original form.

  3. Noise Functions • Atmospheric Turbulence • Picture degraded from atmosphere • Motion Blur • Results from moving the camera while the shutter is open • Salt and Pepper • Random impulse noises • Sinusoidal Noise • Constant Sinusoidal noise

  4. Functions to Restore Images • Salt & Pepper- Median, Adaptive Median, Arithmetic Mean • Atmospheric – Inverse Filtering, Gaussian Low Pass Filter • Motion Blur – Inverse Filtering • Sinusoidal Blur – Butterworth, Gaussian, Ideal Band-pass & Band-reject, Notch Filters.

  5. Original Image Used in Filters A 256x256 Picture of Jesse Taken By the ECE 533 Staff

  6. Salt & Pepper Noise

  7. Salt & Pepper: Results • Best Results: Adaptive Median • Kept Most of Detail While Filtering Most of the Noise. • Worst Results: 7x7 Arithmetic Mean • Didn’t Filter Very Well, Lost Too Much Detail Due to Smoothing.

  8. Atmospheric Turbulence Original Image 2D Fourier Transform Noise Model in Frequency Domain Blurred Image

  9. Filtered Image

  10. Filter Results • As you can see, the filtered results do not vary much from the original. • The values for the inverse filter were too small to get any result, which explains the black image which was output • Gaussian didn’t improve restoration of the image

  11. Sinusoidal Filters – Band-Reject

  12. Sinusoidal Filters – Band-Pass

  13. Sinusoidal Filters – Notch

  14. Conclusion / What We Learned • Discovered Background to Many of the Image Filters We Used • Understand at a Deeper Level of How these Filters and Algorithms Work • Learned More about Signal Processing and how to use the Fast Fourier Transform • Experience with Different Types of Filters, and Which Filters to Use for a Given Noise

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