Image Processing Filter Weight Adjustment for Edge Preservation
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Solution Overview
Problem
The Total Variation (TV) method struggles to effectively separate noise from image edges with small pixel value variations, leading to blur and noise retention around edges, as it fails to distinguish between edge and noise-induced local variations.
Innovation Solution
An image processing method that calculates a local variation amount, adjusts filter weights based on gradient direction and strength features, and applies filtering to preserve edge sharpness by optimizing filter coefficients for each pixel location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the TV method uses a fixed filter coefficient based on local variation amount, then noise reduction is achieved, but edge sharpness deteriorates due to blur in areas with small pixel value variations
Solution Approach 1:
The filter coefficient is made dynamic by adjusting it according to the gradient strength of the pixel value. Instead of using a fixed coefficient based solely on local variation amount, the method dynamically adapts the coefficient to the local gradient characteristics, allowing the filtering strength to vary spatially across the image.
Solution Approach 2:
The method applies different filtering characteristics to different local regions by considering the gradient strength at each pixel location. Areas with strong gradients (edges) receive different treatment compared to areas with weak gradients, achieving local optimization of both noise reduction and edge preservation.
2Object-affected harmful factors
If the TV method applies strong filtering to reduce noise, then noise elimination improves, but edge integrity deteriorates due to blur and noise retention around edges
Solution Approach 1:
The method changes the filtering parameter (filter coefficient) based on the gradient strength parameter. By establishing a relationship between gradient strength and filter coefficient, the system automatically adjusts the filtering intensity to preserve edge integrity while maintaining noise reduction effectiveness.
Solution Approach 2:
The gradient strength information serves as feedback to adjust the filter coefficient. The method calculates the gradient strength from the pixel values and uses this information to modulate the filtering operation, creating a feedback loop that preserves edge characteristics.
Data Source
AI summary
An image processing method separates an input image into a skeleton component, and residual component. In the method a local variation amount, which is a variation amount between a target pixel and a pixel adjacent to the target pixel, is calculated; a skeleton component extraction filter weight is calculated based on the local variation amount; an image feature amount of a gradient direction of a pixel value around the target pixel, and an image feature amount of a gradient strength of the pixel value around the target pixel, are calculated; the skeleton component extraction filter weight is corrected based on these image feature amount; a skeleton component extraction filter coefficient is calculated based on the corrected skeleton component extraction filter weight; and the skeleton component is extracted by applying skeleton component extraction filtering to the target pixel using the calculated skeleton component extraction filter coefficient.