Hyper-Clarity Transform for Image Clarity and Color Balance
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Solution Overview
Problem
Current digital image enhancement techniques face limitations such as color balance issues, halos, ringing, and difficulty in realistically remapping images, particularly when dealing with mixed color illuminants.
Innovation Solution
The Hyper-Clarity Transform (HCT) system and method enhance images through spatially localized tonemapping, multi-resolution image sharpening, and noise filtration, utilizing a HCT engine with components like downsamplers, upsamplers, and chroma adjusters, which work on image pyramid data structures and mimic human perception to improve clarity and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image enhancement techniques are used, then image clarity may be improved, but color balance deteriorates due to mixed color illuminants
Solution Approach 1:
The image processing is segmented into multiple resolution levels using an image pyramid structure. The HCT engine processes the image at different resolutions, allowing selective enhancement of clarity at various scales while preserving color balance through separate processing paths for luminance and chrominance components.
Solution Approach 2:
The hyper-clarity transform applies local quality adjustments by processing different regions of the image with appropriate clarity enhancement while maintaining original color characteristics. The transform selectively enhances edge regions and structural details without uniformly adjusting color balance across the entire image.
2Measurement precision
If current image enhancement techniques are used, then image clarity may be improved, but halos and ringing artifacts are introduced
Solution Approach 1:
The HCT engine dynamically adjusts the clarity enhancement strength based on local image characteristics. The transform adapts its processing intensity in different regions, applying stronger enhancement to edges and structures while reducing enhancement in uniform areas, thereby preventing halo and ringing artifacts from forming.
Solution Approach 2:
The multi-resolution processing provides feedback mechanisms where results from coarser resolution levels inform the processing at finer levels. This feedback loop allows the system to detect and suppress potential artifact formation by comparing enhanced details against the original image structure at multiple scales.
3Measurement precision
If current image enhancement techniques are used, then visual appeal may be improved, but realistic remapping deteriorates
Solution Approach 1:
The hyper-clarity transform changes key processing parameters including resolution level, enhancement strength, and processing scale adaptively. By varying these parameters across different regions and resolution levels, the system achieves enhanced visual appeal while preserving realistic image characteristics through controlled parameter transitions.
Data Source
AI summary
A system, method, and computer program product are provided for enhancing an image utilizing a hyper-clarity transform. In use, an image is identified. Additionally, the identified image is enhanced, utilizing a hyper-clarity transform. Further, the enhanced image is returned.