Anisotropic Multi-Scale Image Contrast Enhancement
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Solution Overview
Problem
Existing multi-scale image processing methods struggle to selectively enhance image features, such as chromosome bands, while preserving edge transitions and orientation, particularly in medical images with sharp grey level transitions like CT images.
Innovation Solution
The method employs anisotropic multi-scale image processing using orientation maps to steer the enhancement of translation difference images and apply anisotropic weighting, allowing for directional enhancement of image features by computing center differences based on local orientations and applying conversion operators to translation differences before summation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conversion functions are excessively non-linear to enhance contrast, then image contrast is improved, but grey value transitions are distorted and artifacts are created
Solution Approach 1:
The patent segments the image processing into multiple scales using a pyramid structure, where each level processes different frequency components. This allows non-linear contrast enhancement to be applied selectively to specific frequency bands rather than the entire image, reducing artifact propagation while maintaining contrast improvement in relevant regions.
Solution Approach 2:
The patent applies different processing characteristics to different regions of the image based on local properties. By computing enhancement parameters locally and applying them in the frequency domain, the method preserves edge sharpness in critical regions while allowing more aggressive contrast enhancement in homogeneous areas, thus improving contrast without creating harmful artifacts.
2Illumination intensity
If multi-scale techniques are applied to CT images to enhance contrast, then image contrast is improved, but edge transitions are distorted and homogeneity is lost
Solution Approach 1:
The patent decomposes the image into a multi-scale pyramid where each level represents different frequency components. By processing each scale separately and using appropriate conversion functions for each level, the method enhances contrast while preserving the structural integrity of edge transitions through controlled reconstruction.
Solution Approach 2:
The patent incorporates feedback mechanisms in the reconstruction process where information from multiple scales is combined with weighting factors that preserve edge characteristics. The reconstruction algorithm uses feedback from the processed detail images to maintain original edge shapes while incorporating enhanced contrast information.
3Ease of manufacture
If isotropic filters are used in decomposition and reconstruction, then processing is simplified, but selective enhancement of oriented features is not possible
Solution Approach 1:
The patent transitions from static isotropic filtering to dynamic anisotropic filtering where the filter characteristics adapt to local image structures. By computing orientation maps and using them to steer the filtering process, the method maintains processing efficiency while gaining the versatility to selectively enhance features in specific orientations and locations.
Solution Approach 2:
The patent changes the parameters of the filtering operation based on local image properties. By computing orientation information and using it to modulate the filter response, the method preserves the simplicity of the overall processing framework while enabling selective enhancement of oriented features through parameter adaptation.
Data Source
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AI summary
At least one approximation image is created of the image at one or multiple scales. Translation difference images are created by pixel-wise subtracting the values of an approximation image at scale s and the values of a translated version of the approximation image. A non-linear modification is applied to the values of the translation difference image (s) and at least one enhanced center difference image at a specific scale is computed by combining the modified translation difference images at that scale or a smaller scale with weights Wi,j. An enhanced image is computed by applying a reconstruction algorithm to the enhanced center difference images. The non-linear modification of the values of the translation difference images is steered by the values of an orientation map which comprises for each pixel a local direction of interest. In addition or alternatively, at least one enhanced center difference image is computed by anisotropic weighing of the enhanced translation differences with weights steered by the orientation map.