👉 Wavelet Multiscale Approximation (WMA) is a computational technique in signal processing and image analysis that leverages wavelet transforms to approximate signals or images at multiple scales or resolutions. Unlike traditional methods that use fixed basis functions, WMA employs a multiscale approach, allowing it to capture both coarse and fine details within the data. This is achieved by decomposing the input signal into different frequency components at various scales, and then reconstructing it using a weighted sum of these scaled wavelets. The WMA algorithm is particularly efficient for tasks such as image compression, denoising, and feature extraction because it can adaptively focus on significant features while discarding less important details, leading to better performance and reduced computational complexity compared to other multiresolution methods.
