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A novel quadratic trigonometric B-spline and Genetic Algorithm (GA) optimize digital image interpolation. This new method enhances image quality and outperforms existing interpolation schemes.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Applied Mathematics

Background:

  • Digital image interpolation is crucial for image resizing and enhancement.
  • Existing methods face challenges in preserving image details and reducing artifacts.
  • Soft computing techniques offer potential for optimizing interpolation parameters.

Purpose of the Study:

  • To develop a new quadratic trigonometric B-spline for improved 2D digital image interpolation.
  • To integrate the B-spline with a Genetic Algorithm (GA) for parameter optimization.
  • To evaluate the proposed interpolation scheme against existing methods using established metrics.

Main Methods:

  • Construction of a novel quadratic trigonometric B-spline with control parameters.
  • Development of an image interpolation scheme utilizing the B-spline and GA.
  • Optimization of B-spline control parameters using the Genetic Algorithm.
  • Quantitative evaluation using Feature SIMilarity (FSIM), Structure SIMilarity (SSIM), Multi-Scale Structure SIMilarity (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR).

Main Results:

  • The proposed B-spline and GA-based interpolation scheme effectively addresses image interpolation challenges.
  • Comparative analysis demonstrates superior performance over three existing interpolation schemes.
  • Objective image quality metrics confirm the enhanced fidelity and detail preservation of the new method.

Conclusions:

  • The novel quadratic trigonometric B-spline combined with GA offers a robust solution for 2D digital image interpolation.
  • The proposed method provides a significant improvement in image quality compared to current techniques.
  • This approach represents a valuable advancement in the field of digital image processing.