Single Wavelet Transform for Image Compression

Single wavelet transform is a widely used technique for image compression. This method works by transforming and decomposing images to remove redundant information, thereby achieving effective image compression. The transform employs multi-resolution analysis to represent images at different scales, where approximation coefficients capture the main image structure while detail coefficients contain finer image details that can be compressed more aggressively. Furthermore, single wavelet transform finds significant application in image fusion processes. It enables the combination of multiple source images into a single composite image while preserving distinct features from each input image. In MATLAB implementation, key functions like wavedec2 perform the 2D wavelet decomposition, wrcoef2 handles coefficient reconstruction, and thresholding functions like wthresh help in compression by eliminating insignificant coefficients. The fusion process typically involves decomposing source images, combining their coefficients using rules like maximum selection or weighted averaging, and reconstructing the fused image using waverec2. This approach maintains critical image characteristics while achieving substantial compression ratios or effective fusion results.