Image Filtering Device Using Adaptive Brightness Weighting
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Solution Overview
Problem
Existing image filtering technologies face challenges in edge preservation and computation complexity, particularly when dealing with high noise levels and varying environmental brightness, as they often generate inappropriate filter weightings and are not easily implementable in hardware.
Innovation Solution
An image-filtering device and method that computes pixel absolute differences, adjusts them based on adaptive brightness parameters, and generates dynamic weighting values to perform convolution, reducing computation complexity and improving edge preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a common edge-preserving filter is used to preserve edges in images, then edge preservation is improved, but computation complexity increases
Solution Approach 1:
The patent changes the parameter representation by using lookup tables (LUTs) to store pre-computed filter coefficients instead of calculating them dynamically. The filter weights are transformed into discrete parameter levels that can be quickly retrieved and applied, significantly reducing computation complexity while maintaining edge preservation quality.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating filter coefficients and storing them in lookup tables before actual image filtering. This preliminary action allows the filtering process to simply retrieve and apply pre-computed values rather than performing complex calculations in real-time, reducing computation complexity while preserving edge details.
2Device complexity
If a common filter uses fixed weightings based on pixel value differences, then computation is simplified, but adaptability to different brightness conditions deteriorates
Solution Approach 1:
The patent applies local quality by creating different filter coefficient sets for different brightness regions. The lookup tables store coefficients adapted to specific brightness conditions, allowing the filter to apply locally-optimal parameters based on the actual image content. This ensures the filter adapts to local brightness variations while maintaining computational efficiency through pre-computed tables.
Solution Approach 2:
The patent introduces dynamics by making filter coefficients adaptive to local image characteristics. Instead of using fixed weightings, the system dynamically selects appropriate filter parameters from lookup tables based on local pixel statistics and brightness conditions. This dynamic adaptation improves versatility while the lookup table structure maintains computational simplicity.
3Adaptability or versatility
If dynamic filter weightings are generated to adapt to different brightness conditions, then adaptability is improved, but computation complexity increases
Solution Approach 1:
The patent performs preliminary computation by pre-calculating filter coefficients for various brightness conditions and storing them in lookup tables. This preliminary action eliminates the need for complex real-time calculations, allowing the system to achieve adaptability through simple table lookups based on local image statistics.
Solution Approach 2:
The patent uses copying by creating multiple pre-computed coefficient sets that are stored in lookup tables. Instead of generating unique dynamic coefficients for each pixel, the system copies appropriate pre-computed coefficients from the LUTs based on matching local image characteristics, significantly reducing computation complexity while maintaining adaptability.
Data Source
AI summary
An image-filtering device for filtering an image that includes a pixel difference computing module, an adaptive brightness adjusting module, a weighting computing module and a filter computing module is provided. The pixel difference computing module uses any one of the pixels as a central pixel within a pixel window to compute pixel absolute differences between the central pixel and every pixels within the pixel window. The adaptive brightness adjusting module multiplies each of the pixel absolute differences with an adjusting parameter to generate adjusted pixel absolute differences. The weighting computing module generates weighting values according to the adjusted pixel absolute differences. The filter computing module performs convolution according to the pixel value of each of the pixels within the pixel window and the corresponding weighting values to generate a filtering result of the central pixel.