Edge Emphasis Apparatus Using Local Brightness Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional edge region detection methods struggle to effectively emphasize edges in images with varying brightness and noise levels, often amplifying noise and failing to adjust edge gain appropriately for different image regions, leading to uneven edge emphasis and excessive noise cancellation.
Innovation Solution
An image generating apparatus and method that extracts edge regions, calculates region-specific edge gains and thresholds based on brightness variation and complexity, and combines these with the original image to produce a final image that emphasizes edges while minimizing noise, using a controller, edge extractor, edge gain calculator, and edge threshold calculator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge gain is increased to emphasize edges in the original image, then edge visibility is improved, but noise amplification increases
Solution Approach 1:
The patent divides the image into multiple regions based on brightness characteristics (bright regions and dark regions) and applies different edge gain values to each region. Bright regions receive lower edge gain to suppress noise amplification, while dark regions receive higher edge gain to maintain edge visibility. This local differentiation resolves the contradiction by adapting the edge emphasis strength to the specific characteristics of each image region.
2Object-generated harmful factors
If noise cancellation is applied to the original image, then noise amplification is reduced, but edge emphasis capability deteriorates
Solution Approach 1:
The patent segments the image processing into distinct stages: first extracting edge regions, then classifying pixels into bright and dark regions, and finally applying region-specific edge gain and thresholding. This segmentation allows the system to preserve edge information while selectively suppressing noise in bright regions, resolving the contradiction between noise cancellation and edge emphasis capability.
3Object-generated harmful factors
If edge gain is adjusted based on bright region characteristics, then noise cancellation is improved in bright regions, but edge emphasis in dark regions deteriorates
Solution Approach 1:
The patent implements local quality by calculating and applying different edge gain values specifically tailored to each region's brightness characteristics. Dark regions receive higher edge gain to compensate for their naturally lower visibility, while bright regions receive lower edge gain to prevent noise amplification. This region-specific adaptation ensures optimal edge emphasis in dark regions without compromising noise cancellation in bright regions.
Data Source
AI summary
An image generating apparatus and method for emphasizing an edge, based on image characteristics by extracting an edge region from an input original image, and an edge gain indicating a degree for emphasizing the edge region and an edge threshold indicating a degree of complexity between pixels are calculated for each of a plurality of image regions of the original image. The extracted edge region is combined with the calculated edge gain. Thresholding is performed by adjusting the calculated edge threshold for the edge region combined with the edge gain, and the thresholding-applied edge region is combined with the original image to output a final image. By adaptively adjusting an edge gain and an edge threshold for a portion where an edge needs to be emphasized and the other portions in an image having multiple image characteristics, both edge emphasis and noise cancellation can be achieved together.