Two-Dimensional Stationary Wavelet Transform for Images

This implementation handles the two-dimensional stationary wavelet transform (SWT) for images, encompassing both decomposition of source images and their subsequent reconstruction. During the 2D SWT process, the source image first undergoes decomposition through wavelet filters applied along rows and columns, producing wavelet coefficients at different scales and orientations (typically approximation, horizontal, vertical, and diagonal details). The reconstruction phase involves applying inverse wavelet transforms to these coefficients using appropriate synthesis filters, effectively restoring the image to its original state. The stationary wavelet transform maintains translation invariance by omitting downsampling operations, making it particularly valuable for image analysis tasks. Through 2D SWT implementation, developers can better analyze frequency-domain characteristics of images and leverage this transform for significant applications in image processing and computer vision, such as feature extraction, denoising, and texture analysis. Key functions typically include wavelet filter bank selection, convolution operations for coefficient calculation, and iterative reconstruction algorithms.