Implementing 2D Wavelet Transform Using DWT

In this documentation, we implement the 2D wavelet transform using Discrete Wavelet Transform (DWT). This method proves exceptionally effective as it enables multiple functionalities and applications through code implementation. By employing DWT, we can perform in-depth analysis and processing of images and signals using algorithms like multi-level decomposition and reconstruction, thereby extracting more valuable information. The implementation typically involves key functions such as wavelet filters (e.g., Haar, Daubechies) and convolution operations for both rows and columns. Furthermore, DWT finds applications in areas like data compression through thresholding techniques and noise elimination using denoising algorithms, providing enhanced options and flexibility. The core algorithm operates by applying 1D wavelet transform sequentially to rows and columns, creating approximation and detail coefficients. In summary, DWT serves as a highly useful and powerful tool with significant code-driven implementations across numerous technical domains.