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Learn Super-Resolution Techniques | Image Processing with OpenCV
Computer Vision Essentials

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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.

Task

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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 sr variable;
  • Read model from the model_path;
  • Set the name espcn and 4x scale;
  • Apply DNN super-resolution method and store result in dnn_image.

Solution

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book
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.

Task

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 sr variable;
  • Read model from the model_path;
  • Set the name espcn and 4x scale;
  • Apply DNN super-resolution method and store result in dnn_image.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

close

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Completion rate improved to 3.45

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