4. Explain is interpolation (upsample) in the context of image processing, and why is it important when increasing the resolution of an image?
Answer:
Interpolation is the process of estimating new pixel values in an image to increase its resolution. This is known as upsampling. When we zoom into an image or convert it to a higher resolution, new pixel positions are introduced that don’t have defined intensity values. Interpolation kernels are used to estimate these values based on existing pixels.
Why is Interpolation Important?
- Prevents blockiness or pixelation in zoomed images
- Preserves edges and smooth transitions in higher-resolution images
- Essential in image editing, scaling, resampling, and reconstruction
Mental Models of Interpolation
(a): Kernel is centered at each input sample
(b): Kernel is centered at each output sample (Polyphase form)
Types of Interpolation Kernels
1. Linear Interpolation (Tent Function)
- Produces piecewise linear interpolation
- May cause creases in smooth regions
3. Windowed Sinc Interpolation
- Best quality interpolator
- Preserves high-frequency details and avoids aliasing
- Can cause ringing artifacts due to repeated frequency components
- Preferred in high-accuracy filtering and professional photo editing
Applications and Preferences
- Graphics Cards: Bilinear + Mipmap (Section 3.5.3)
- Photo Editing Tools: Bicubic
- Advanced Users: Windowed Sinc, Global Optimization, Neural Hallucination
Use of Splines in Vision
Splines are used for:
- Geometric modeling
- Motion estimation
- Surface interpolation
- Elastic deformations (Section 3.6.2)
Splines offer:
- Derivative control
- Efficient evaluation
- Compatibility with multi-resolution frameworks