Wavelet Signal Denoising via Variable Thresholds
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Solution Overview
Problem
Existing signal denoising techniques often distort the original signal, are time-consuming, or ineffective for real-time analysis, particularly in addressing noise in audio and video signals, which can lead to inaccurate data representation, download interruptions, and increased bandwidth requirements.
Innovation Solution
A method involving discrete wavelet transformation to separate signal and noise components, using variable thresholds for each resolution level, and selective coefficient modification to generate a denoised signal through inverse transformation, suitable for both audio and video signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If filtering techniques are used to smooth noisy signals, then noise reduction is achieved, but the signal is distorted
Solution Approach 1:
The patent segments the signal processing into multiple resolution levels through wavelet decomposition. The signal is divided into different frequency bands (approximation and detail coefficients) at multiple scales, allowing selective noise removal at each level while preserving signal characteristics. This segmentation enables targeted denoising without uniform filtering that distorts the entire signal.
Solution Approach 2:
The patent applies local quality by using resolution-level dependent thresholds. Different threshold values are applied to different resolution levels (k=1 to K), where coarser levels use different thresholds than finer levels. This allows the denoising process to adapt to local signal characteristics at each scale, preserving important signal features while removing noise appropriately at each resolution level.
2Object-affected harmful factors
If conventional denoising methods are used, then noise is reduced, but computation time increases
Solution Approach 1:
The patent performs preliminary action by pre-determining the optimal number of decomposition levels (K) and pre-calculating resolution-level dependent thresholds before actual denoising. The method establishes the decomposition structure and threshold values in advance, which streamlines the subsequent denoising process and reduces real-time computation requirements while maintaining effective noise removal.
3Ease of operation
If fixed thresholding is applied across all resolutions, then processing is simplified, but denoising effectiveness decreases
Solution Approach 1:
The patent implements dynamics by transitioning from fixed thresholding to dynamic, resolution-level dependent thresholding. The threshold value varies with the resolution level (k), allowing the denoising process to adapt to the characteristics of signals at different scales. This dynamic approach improves denoising accuracy by accounting for the fact that noise and signal characteristics differ across resolution levels, while still maintaining a systematic processing framework.
Data Source
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AI summary
Methods, systems and programs for denoising a signal using discrete wavelet transformation are provided. For example, a method for denoising a signal may include determining a number of resolution levels to denoise, determining variable threshold(s) for each resolution level, applying the determined variable threshold(s) to denoise at least a detail component of each of the determined resolution levels. Each variable threshold includes a separately determined lower threshold and upper threshold. The method for denoising a signal may further include transforming, using an inverse discrete wavelet transformation, at least the denoised detail component for each of the determined resolution levels into a denoised signal.