Digital Filter Weight Coefficient Adaptation for Image Edge Preservation
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Solution Overview
Problem
Existing digital filters rely solely on space coordinates to calculate weight coefficients, leading to noise reduction issues and edge blurring in images, and excessive enhancement near image edges due to lack of adaptability to image content.
Innovation Solution
A method and apparatus for acquiring a weight coefficient of a digital filter by extracting global and local block features from image blocks, calculating an image block distance, and using the exponential of this distance as the weight coefficient, enabling self-adaptation to image content and utilization of redundant texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If weight coefficient is calculated using only space coordinates of pixels, then calculation is simple, but processing effect deteriorates with edge blurring and noise reduction issues
Solution Approach 1:
The patent changes the parameters used for weight coefficient calculation from only space coordinates to include image content features (strength values, gradients, textures). This allows the filter to adapt to local image characteristics, improving processing precision while maintaining reasonable calculation complexity through efficient feature extraction methods.
Solution Approach 2:
The patent makes the weight coefficient dynamic by allowing it to change according to local image content characteristics. Instead of fixed spatial weights, the coefficients adapt to local features such as edges, textures, and noise patterns, enabling the filter to optimize performance for different image regions.
2Manufacturing precision
If weight coefficient is made adaptive to image content, then processing effect improves, but calculation complexity increases
Solution Approach 1:
The patent applies local quality by making the weight coefficient adaptive to local image content characteristics. Different regions of the image receive different weight coefficients based on their specific features (edges, textures, noise levels), improving processing precision locally while using efficient feature extraction to control overall calculation complexity.
3Device complexity
If linear filter uses fixed weight coefficient function of space coordinates, then device complexity is low, but adaptability to image content deteriorates
Solution Approach 1:
The patent transforms the static weight coefficient function into a dynamic one that adapts to image content. The weight coefficient becomes a function of both spatial position and local image features, enabling the filter to automatically adjust to different image regions while maintaining a relatively simple overall structure through efficient feature extraction and weighting mechanisms.
Data Source
AI summary
Embodiments of the present invention provide a method and an apparatus for acquiring a weight coefficient of a digital filter so as to enhance a processing effect of images or videos and reduce the complexity of operations. The method includes: extracting global block features and local block features of image blocks, where the image blocks include a first image block and a second image block; acquiring an image block distance fs between the first image block and the second image block according to a global block feature and local block features of the first image block as well as a global block feature and local block features of the second image block; and evaluating a value of e fs/θ2.