Computational Photography
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1. Pixels and Linear Filters
1. Light comes out from the sources
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2. Frequency
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3. Template Matching
- $1 \rightarrow D$
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4. Denoising and Compression
- $FFT$ can be used to efficiently implement $SSD$
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5. Light
- We perceives color from the complex interacction of multiple factors
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6. Texture Synthesis
- Create new samples of a given texture
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7. Graph Cut-Based Segmentation
- Good region is similar to foreground color model and dissimilar from background color
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8. Histogram Equalization
- Reassign values so that the number of pixels with each values is more evenly distributed
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9. Image Compositing
- small segmentation errors noticeable
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10. Image Warping
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11. Image Morphing
- Affine transformation
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12. Pinhole Camera
- Angless and length are lost
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13. Single-view Metrology and Cameras
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14. Color Spaces
**RGB** stands for **Red–Green–Blue**, an **additive color model** used in displays, cameras, and digital imaging.
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Lab: Color Swapping
- Substituting a specific color in an image with some targeted color for the purpose of exploration, design, image, and creation.
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Lab: Contrast Enhancement
- a photometric measure of the [phtometric](<https://en.wikipedia.org/wiki/Photometry_(optics)>) measure of the **luminous intensity** per unit area o...
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Lab: Error Path Quilting
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Lab: Hybrid Images
- Two different interpretations of a picture can be perceived by
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Lab: Image Filtering
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Lab: Image Formation
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