Noise Reduction API design, including additional noise reduction models beyond the Wiener filter—such as median filter, Gaussian/Bilateral filters, non-local means (NLM), wavelet-based denoising, and deep learning methods. The overall API structure remains the same; we simply elaborate on possible algorithms and how they fit into the design.

1. Overview

1. Purpose
   • Provide endpoints to denoise or clean signals, images, or audio data using a variety of noise reduction methods.
   • Support both classic signal-processing filters (e.g., Wiener, Median, Gaussian, Bilateral, Non-Local Means, Wavelet) and advanced ML-based approaches (e.g., Autoencoders, CNN-based denoisers).
2. Data Flow
   1. Data Ingestion: System receives raw, noisy data (e.g., images, audio waveforms, time-series signals).
   2. Training/Parameter Tuning (optional): Some filters are parameter-based (e.g., Wiener, wavelet thresholding) or require training (e.g., deep learning).
   3. Noise Reduction: Pass in data to be denoised using a specific filter/model.
   4. Result Retrieval: Retrieve the denoised output and metadata (filter used, parameters, performance metrics).

2. Common Noise Reduction Models

Below is a non-exhaustive list of noise reduction algorithms that might be available in your service:

1. Wiener Filter
   • Linear filter that minimizes MSE between estimated and original signals.
   • Often requires noise variance or power spectrum estimates.
2. Median Filter
   • Non-linear filter that replaces each pixel (or sample) with the median of its neighborhood.
   • Great for “salt-and-pepper” noise.
3. Gaussian Filter
   • Linear, convolution-based smoothing filter using a Gaussian kernel.
   • Blurs edges but is fast and easy to configure (just need a kernel size and sigma).
4. Bilateral Filter
   • Non-linear, edge-preserving filter that considers both spatial distance and intensity difference.
   • Smooths homogeneous areas while preserving edges.
5. Non-Local Means (NLM)
   • Compares patches across the entire image (or signal) to find similar patches and average them, thus reducing noise while preserving details.
6. Wavelet-Based Denoising
   • Applies a wavelet transform, thresholds high-frequency coefficients, and then reconstructs.
   • Preserves sharp features often better than simple spatial smoothing.
7. Deep Learning Approaches
   • Denoising Autoencoders, CNNs, Transformers for image/signal denoising.
   • Typically require a training set of noisy and (optionally) clean data.

3. API Endpoints

3.1. Data Ingestion

Endpoint:

POST /api/v1/noise-reduction/data