DWT & IDWT Interactive Simulator

Understand signal decomposition and reconstruction through visualization.

1

Define Your Signal \( x[n] \)

2

Discrete Wavelet Transform (Analysis)

Using the Haar Wavelet: \( h = [0.5, 0.5] \) (Low-pass) and \( g = [0.5, -0.5] \) (High-pass).

Approximation (A)

Low-frequency: Captures the "trend".

Detail (D)

High-frequency: Captures "edges" or "noise".

3

The "Magic" of Wavelets

Try silencing the Detail coefficients. In JPEG2000 or audio compression, small detail values are often set to zero to save space.

Watch the Reconstructed signal change below!
4

Inverse DWT (Synthesis)

Mathematically: \( x[n] = \text{upsample}(A) * h_{inv} + \text{upsample}(D) * g_{inv} \)