Super-resolution techniques can be broadly categorized into:

• Traditional interpolation-based methods (Bilinear, Bicubic, Lanczos);

• Deep learning-based super-resolution (CNNs, GANs, Transformers).

Traditional Interpolation-Based Methods

Interpolation is one of the simplest approaches to super-resolution, where missing pixels are estimated based on surrounding pixel values. All common interpolation techniques include cv2.resize(), but the interpolation parameter differs:

Nearest-Neighbor Interpolation

• Copies the closest pixel value to the new location;

• Produces sharp but blocky images;

• Fast but lacks smoothness and detail.

Bilinear Interpolation

• Averages four neighboring pixels to estimate the new pixel value;

• Produces smoother images but can introduce blurriness.

Bicubic Interpolation

• Uses a weighted average of 16 surrounding pixels;

• Provides better smoothness and sharpness compared to bilinear interpolation.

Lanczos Interpolation

• Uses a sinc function to calculate pixel values;

• Offers better sharpness and minimal aliasing.

While interpolation-based methods are computationally efficient, they often fail to restore fine details and textures.

Deep Learning-Based Super-Resolution

Pretrained Super-Resolution Models:

• ESPCN (Efficient Sub-Pixel Convolutional Network): Fast and efficient for real-time SR;

• FSRCNN (Fast Super-Resolution CNN): A lightweight network optimized for speed;

• LapSRN (Laplacian Pyramid SR Network): Uses progressive upscaling for better details.