Unified Frequency Transform for Image Artifact Reduction
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Solution Overview
Problem
Current digital image enhancement techniques face challenges such as halos, ringing, gradient reversal, flatness, and unrealistically remapped images, failing to provide flexible full-spatial frequency control, noise filtering, and effective visualization of 3D and 1D data, particularly in applications like medical imaging and geological exploration.
Innovation Solution
The Unified Frequency Transform (UFT) method and engine, which performs spatially localized tonemapping, multi-resolution contrast enhancement, noise filtration, and gamut correction independently or in combination, using Programmable Unit Interval Transforms (PUITs) for flexible control, and can be extended to various dimensions for enhanced visualization and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image enhancement techniques are used, then image clarity may be improved, but artifacts such as halos, ringing, and gradient reversal occur
Solution Approach 1:
The image processing is segmented into multiple frequency bands using a frequency transform, allowing independent processing of different spatial frequencies. This segmentation enables precise control over enhancement at each frequency level while avoiding the generation of artifacts that occur with uniform processing across all frequencies.
Solution Approach 2:
The patent applies different processing parameters and enhancement strategies to different spatial frequency regions and image regions. By adapting the processing characteristics to local image properties and frequency content, the system achieves enhanced clarity without introducing uniform artifacts across the entire image.
2Adaptability or versatility
If conventional enhancement methods are applied, then some image qualities are improved, but flexible full-spatial frequency control is not provided
Solution Approach 1:
The system dynamically adjusts processing parameters for each spatial frequency band based on the image content and desired enhancement goals. This dynamic control allows flexible manipulation of different frequency components independently, providing adaptability while maintaining high enhancement quality through optimized processing at each frequency level.
Solution Approach 2:
The patent changes processing parameters selectively across different spatial frequencies, allowing independent optimization of enhancement characteristics for each frequency band. This parameter variation enables both flexible control and high enhancement quality by tailoring the processing to the specific requirements of each frequency component.
3Measurement precision
If noise filtering is applied, then image quality improves, but processing time and computational resources increase
Solution Approach 1:
Noise filtering is applied selectively to specific frequency bands where noise is most prominent, rather than processing the entire image uniformly. This segmented approach reduces computational complexity while maintaining effective noise reduction quality by concentrating processing resources where they are most needed.
Solution Approach 2:
The system applies noise filtering at the appropriate level of intensity and scope for each frequency band, avoiding excessive processing that would increase computational burden unnecessarily. By applying partial filtering where sufficient and full filtering where needed, the system achieves good noise reduction with optimized processing time.
Data Source
AI summary
Methods and apparatus for enhancing optical images and parametric databases are disclosed. In an exemplary embodiment, a method includes identifying an image and deconstructing the image into a frequency-based spatial domain representation utilizing a pyramidal data structure including a plurality of levels on a frequency-by-frequency basis. The method also includes modifying the frequency-based spatial domain representation to generate a modified frequency-based spatial domain representation, reconstructing an enhanced image from the modified frequency-based spatial domain representation, and returning the enhanced image. In an exemplary embodiment, an apparatus includes a deconstructor that deconstructs an image into a frequency-based spatial domain representation utilizing a pyramidal data structure including a plurality of levels on a frequency-by-frequency basis and a modifier that modifies the frequency-based spatial domain representation to generate a modified representation. The apparatus also includes a reconstructor that reconstructs an enhanced image from the modified representation and returns the enhanced image.