Signal Processing Noise Reduction Edge Preservation
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Solution Overview
Problem
Existing noise reduction techniques often alter high-frequency components, such as edges, in signals, which is undesirable, especially in image processing where edge preservation is crucial, and they are computationally demanding, making them inefficient for real-time video processing.
Innovation Solution
A method involving two noise reduction processes applied in opposite directions along the same coordinate, with weighting adjustments based on edge detection to preserve edges, allowing for substantial noise removal while minimizing computational steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise reduction techniques are applied to remove noise from signals, then noise is reduced, but edges and high frequency components are altered or blurred
Solution Approach 1:
The patent applies different processing strategies to different regions of the signal based on local characteristics. Edge-preserving filters are applied specifically at edge locations detected through gradient analysis, while standard noise reduction is applied in non-edge regions. This local differentiation allows noise removal without blurring edges, as each region receives treatment appropriate to its characteristics.
Solution Approach 2:
The signal processing is segmented into distinct stages: edge detection phase, filtering phase, and reconstruction phase. By separating edge preservation from noise reduction operations, the method avoids the trade-off present in conventional approaches where a single filter must balance both concerns. The segmentation allows independent optimization of noise removal and edge sharpness.
2Manufacturing precision
If edge-preserving noise reduction techniques are applied to maintain sharp edges, then edge quality is improved, but computational complexity increases significantly
Solution Approach 1:
The patent performs preliminary edge detection and classification before applying noise reduction filters. By identifying edge locations and orientations in advance, the method avoids computationally expensive operations in non-edge regions. This preliminary action allows standard efficient filters to be used where appropriate, reducing overall computational complexity while maintaining edge sharpness where needed.
Solution Approach 2:
Complex edge-preserving filtering operations are applied only locally at detected edge positions, while simpler and faster filters are used in non-edge regions. This localized application of complex algorithms significantly reduces the overall computational burden compared to applying edge-preserving filters to the entire signal, while still achieving the desired edge preservation效果.
3Object-affected harmful factors
If multi-directional filtering is applied to reduce noise from all directions, then noise reduction effectiveness is improved, but processing time increases
Solution Approach 1:
The patent applies noise reduction filtering selectively rather than uniformly in all directions. By using edge detection to identify where filtering is most needed, the method performs partial action concentrated at critical locations rather than excessive action across the entire signal. This reduces processing time while maintaining effective noise reduction at edge regions where it matters most.
Solution Approach 2:
The method performs preliminary analysis to determine the noise characteristics and signal structure before applying multi-directional filtering. This allows the algorithm to adaptively select filtering directions and intensities, avoiding unnecessary computations in regions where noise is minimal or where filtering would harm signal features. The preliminary action optimizes the filtering process to achieve effective noise reduction with reduced processing time.
Data Source
AI summary
Described herein is a method for signal processing in which noise in an input signal comprising an intensity which is a function of at least a first coordinate and a second coordinate. A noise reduction or de-noising process is applied to the input signal in respect of the first coordinate to generate an intermediate de-noised signal. A second noise reduction or de-noising process is applied to the intermediate de-noised signal in respect of the second coordinate to generate an output de-noised signal. For each coordinate, noise reduction processes are applied in two directions, the results of these processes being averaged to provide the de-noised signals. Weighting is applied in accordance with the detection of an edge within the signal used as input in the de-noising process.