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Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.

A K Bhandari1, V Soni1, A Kumar1

  • 1PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482011, MP, India.

ISA Transactions
|June 5, 2014
PubMed
Summary

This study introduces a novel contrast enhancement method for satellite images using Cuckoo Search (CS) and Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD). The approach significantly improves image quality metrics compared to existing techniques.

Keywords:
Adaptive learningCS algorithmDWT–SVDImage equalization and remote sensing image contrast enhancementWavelet thresholding

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computational Intelligence

Background:

  • Low contrast in satellite imagery hinders accurate analysis and interpretation.
  • Existing enhancement techniques often struggle to preserve fine details and introduce artifacts.

Purpose of the Study:

  • To develop an advanced contrast enhancement method for low contrast satellite images.
  • To leverage the Cuckoo Search algorithm and DWT-SVD for superior image quality improvement.

Main Methods:

  • Decomposition of input images into frequency subbands using Discrete Wavelet Transform (DWT).
  • Optimization of DWT subbands via the Cuckoo Search (CS) algorithm.
  • Singular Value Decomposition (SVD) applied to the low-low thresholded subband for intensity information extraction.
  • Reconstruction of the enhanced image using Inverse Discrete Wavelet Transform (IDWT).

Main Results:

  • The proposed CS-DWT-SVD method demonstrated enhanced performance.
  • Quantitative analysis showed improvements in Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean, and Standard Deviation.
  • The method outperformed conventional and state-of-the-art contrast enhancement techniques.

Conclusions:

  • The Cuckoo Search algorithm effectively optimizes DWT subbands for contrast enhancement.
  • The DWT-SVD based approach provides superior quality improvement for low contrast satellite images.
  • This method offers a robust solution for enhancing satellite image visual quality and analytical utility.