Week 1 bits and intensity
- spatial resolution
- interpolation
Week 2 Contrast Stretching
- gamma (choose a filter for image)
- histogram (pseudo code and api)
- contrast
- dynamic range (num of distinct intensity of an image)
- cumulated histogram (from dynamic range) equalization
- given different cumulated histogram, choose the curve of equalization
Week 3 Histogram Matching
- how to match a histogram
- linear spatial filter (correlation and convolution)
- under what condition correlation and convolution are the same?
- smoothing (lowpass: gaussian, average, medium…) and sharping (highpass, high boost, )
Week 4 Fourier Transformation / IFT
- impulse train
- sampling theorem (the frequency of sampling should be two times of …)
- DFT / IDFT (given one function to calculate the DFT/IDFT)
- no matter IDFT and IFT will result in complex, spectrum, log, magnitude/phase angle
- translation insensitive, rotation sensitive
- filters (ideal lowpass, Butterworth, gaussian, corresponding highpass filters)
Week 5 Image Restoration
- degradation model, the formula of additive noise
- noise distribution
- mean filter (contra-harmonic, salt and pepper)
- for example given a salt noise, how to define a contra-harmonic filter
- alpha-trim filter, when will become mean filter
- adaptive filter (local, three situation and different situation)
- given example of local variance and global, which filter should you choose
- adaptive medium filter
- bilateral
Week 6 Edge Detection
- smooth (why should we do smoothing)
- trade off between localization and detection
- Hague transformation
- line detection
- interest point (which is a good interest point)
- why we are so interest in the corner detection
- harris corner detection (familiar with the whole process:loss function/ structure tensor identify what it is)
Week 7 Brightness Consistency Equation
- brightness consistency equation
- Lucas – Kanade
- interactive refinement
- coarse-to-fine optical flow
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