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ECE 533 Project Tribute. By: Justin Shepard & Jesse Fremstad. 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. Noise Functions. Atmospheric Turbulence
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ECE 533 Project Tribute By: Justin Shepard & Jesse Fremstad
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.
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
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.
Original Image Used in Filters A 256x256 Picture of Jesse Taken By the ECE 533 Staff
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.
Atmospheric Turbulence Original Image 2D Fourier Transform Noise Model in Frequency Domain Blurred Image
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
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