Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding

• We introduce a wavelet adaptive-denoising architecture for real-time 1D-systems.
• The Perfect Reconstruction of the wavelet transform is satisfied.
• It includes five blocks: dwt, idwt, median, thresholding and delay.
• The adaptive threshold is based on a real-time sorting process.
• The quantization error, delay and latency are better than in related works.

This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the biorthogonal base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.