Textbook followed: Digital Image Processing, 3rd edition Rafael C. Gonzales
Professor: Fred Fontaine
Independent study on digital image processing with a focus on computer vision and super-resolution. I mainly followed Gonzales' Digital Image Processing, reading through 1-2 sections a week, and towards the end switched to reading research papers in my areas of interest. Throughout the semester, I proceeded to implement a number of the algorithms I read about, which can be seen in the notebooks provided in the repository.
Primary tools: OpenCV, Pytorch
- Sampling
- Pixel relationships
- Linear algebra
- Probability
- Color spaces
- Intensity transformations
- Histogram processing
- Histogram matching
- Spatial filters
- Smoothing/Sharpening filters
- Sampling
- Aliasing issues
- DFT
- 2D FTs
- Frequency based smoothing/sharpening
- Selective filters
- Color spaces
- Color transformations
- Colored Smoothing/sharpening
- Noise in color images
- Color-based segmentation
- Noise models:
- Gaussian
- Rayleigh
- Gamma
- Exponential
- Uniform
- Impulse
- Periodic noise
- Estimating noise
- Spatial filtering for denoising
- Frequency domain filtering for denoising
- Estimating noise
- Adaprive filters for noise reduction
- MSE filtering
- Least square filtering
- Erosion
- Dilation
- Opening
- Closing
- Hit-or-Miss
- Boundary extraction
- Hole filling
- Convex hull
- Thickening
- Thinning
- Skeletons
- Grey-scale morphology
- Point detection
- Line detection
- Gradients
- Edge detection
- Marr-Hildreth
- Canny
- Edge models
- Thresholding
- Dam construction
- Quaternions
- Velocity fields
- Perspective geometry
- Photonic image formation
- Stereo
- Info theory
- Lossless compression
- Wavelets
- Transform coding