Learn how to remove impulsive noise from signals using pursuit methods.

Learn how to denoise a signal and reproduce the results in your workspace.

Use wavelets to denoise signals and images. Because wavelets localize features in your data to different scales, you can preserve important signal or image features while removing noise. The basic idea behind wavelet denoising, or wavelet thresholding, is that the wavelet transform leads to a sparse representation for many real-world signals and images. What this means is that the wavelet transform concentrates signal and image features in a few large-magnitude wavelet coefficients. Wavelet coefficients which are small in value are typically noise and you can "shrink" those coefficients or remove them without affecting the signal or image quality. After you threshold the coefficients, you reconstruct the data using the inverse wavelet transform.

The example shows how to denoise a signal using interval-dependent thresholds.

Starting from a given image, the goal of true compression is to minimize the number of bits needed to represent it, while storing information of acceptable quality. Wavelets contribute to effective solutions for this problem. The complete chain of compression includes iterative phases of quantization, coding, and decoding, in addition to the wavelet processing itself.