Two-Dimensional Discrete Wavelet Transform for Image Processing

MATLAB 2K 195 views 0 downloads 1 credits
Tags:

Resource Overview

Perform multi-level (≥3) 2D discrete wavelet transformation on images, reconstruct the transformed data, and calculate the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image. Implementation typically involves wavelet decomposition using functions like wavedec2(), reconstruction using waverec2(), and PSNR calculation through mean squared error computation.

Detailed Documentation

In this process, we perform a two-dimensional discrete wavelet transform on the input image. The transformation requires a minimum of 3 decomposition levels to ensure finer analysis and reconstruction of image components. Key implementation steps include selecting appropriate wavelet filters (e.g., Daubechies or Haar wavelets), using multi-level decomposition to separate image details at different resolutions, and applying inverse transformation for reconstruction. After reconstructing the image from wavelet coefficients, we calculate the Peak Signal-to-Noise Ratio (PSNR) as a critical metric for evaluating reconstruction quality. The PSNR computation involves comparing the reconstructed image with the original through mean squared error analysis, providing quantitative assessment of processing accuracy. This comprehensive approach enables thorough evaluation of image processing effectiveness and precision.

You May Also Like