Super-Resolution Techniques
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.
Swipe to start coding
You are given an image with low resolution:
- Apply bicubic interpolation method with 4x scale and store result in
bicubic_image; - Define and create deep neural network object in
srvariable; - Read model from the
model_path; - Set the name
espcnand 4x scale; - Apply DNN super-resolution method and store result in
dnn_image.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
