Noise Filter Using Segmented Edge Detection for Image Quality
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Solution Overview
Problem
Conventional noise filters deteriorate image quality by smoothing entire images, failing to differentiate between high-frequency detail areas and noise components, leading to reduced resolution and image quality degradation.
Innovation Solution
A noise filter comprising a first edge detector to identify high-frequency component areas and a second bilateral filter to perform noise filtering only on remaining areas, while conserving the high-frequency components, with a function processor controlling the operation of these filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a noise filter performs smoothing on the entire image to remove noise, then noise reduction is improved, but image resolution and quality deteriorate
Solution Approach 1:
The image is segmented into different regions based on local variance: high-variance regions (edges, textures, details) and low-variance regions (smooth areas). Different filtering strategies are applied to each segment, allowing noise reduction in smooth areas while preserving details in high-variance areas, thus resolving the contradiction between noise reduction and resolution preservation
Solution Approach 2:
The filtering operation adapts its strength based on local image characteristics. In high-variance regions, minimal or no filtering is applied to preserve edges and textures. In low-variance regions, stronger filtering is applied to remove noise. This local adaptation allows the system to achieve good noise reduction overall while maintaining high resolution in critical areas
2Device complexity
If a noise filter smooths the entire image without differentiation, then noise removal is simplified, but detail areas and textures are degraded
Solution Approach 1:
The image processing is segmented into two distinct passes: first identifying high-variance regions that contain important details, then applying noise filtering only to low-variance regions. This segmentation allows the system to maintain simplicity in the filtering operation while intelligently preserving details through regional differentiation
Solution Approach 2:
Before applying the noise filter, the system performs a preliminary analysis to identify high-variance regions containing edges and textures. This preliminary action marks or flags these areas so that the subsequent filtering operation can avoid them, ensuring detail preservation without complicating the actual filtering process
Data Source
AI summary
A noise filter according to an embodiment includes: a first filter that functions as an edge detector to detect a high-frequency component area of an image; a second filter that performs a noise filtering function for the remaining areas of the image while conserving the high-frequency component area detected by the first filter; and a function processor that controls operations of the first filter and the second filter. According to an embodiment, since noise filtering for only a noise component area is performed by dividing the image into a high-frequency component area and a noise component area, it is possible to minimize deterioration of the high-frequency area and improve the resolution and quality of the image.