Multi-Scale Image Enhancement for Local Contrast Without Artifacts
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Solution Overview
Problem
Images captured by mobile devices often suffer from low contrast, and users lack the knowledge or inclination to manually enhance them.
Innovation Solution
A method involving downsampling, global contrast enhancement, texture map generation, clipped histogram creation, lookup table generation, and blending of locally and globally contrast-enhanced frames to achieve multi-scale image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If global contrast enhancement is applied to the entire image, then overall image contrast is improved, but local details and textures may be lost or distorted
Solution Approach 1:
The patent divides the image into multiple local regions or tiles, and applies contrast enhancement independently to each region based on its local histogram characteristics. This segmentation allows different contrast adjustment parameters to be applied to different areas, preserving local details while improving overall contrast.
Solution Approach 2:
The patent implements locally-adaptive contrast enhancement by computing histogram statistics for each local region and applying region-specific lookup tables (LUTs). This ensures that each local area receives customized contrast adjustment tailored to its specific texture and content characteristics, rather than a uniform global adjustment.
2Manufacturing precision
If multi-scale processing is applied to enhance local contrast, then local detail enhancement is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent introduces multi-scale processing by applying contrast enhancement at different spatial resolutions or scales. By processing the image at multiple scales and combining the results, the method achieves superior local detail enhancement while managing computational complexity through hierarchical processing.
Solution Approach 2:
The patent employs adaptive processing where the enhancement parameters and processing intensity dynamically adjust based on local image characteristics such as texture complexity and frequency content. This dynamic adaptation allows the system to apply more aggressive enhancement only where needed, reducing overall computational burden.
3Illumination intensity
If aggressive contrast enhancement is applied, then image contrast is significantly improved, but artifacts and stains appear in the enhanced regions
Solution Approach 1:
The patent applies preprocessing steps such as noise reduction and texture preservation filters before contrast enhancement to prevent artifact formation. By preparing the image data in advance with protective measures, the subsequent aggressive contrast enhancement can be applied without generating harmful artifacts or stains in the enhanced regions.
Data Source
AI summary
A method includes downsampling an input frame and performing global contrast enhancement of the input frame based on the downsampled input frame to generate a globally contrast-enhanced frame. The method also includes generating texture maps at different scale resolutions based on the downsampled input frame. The method further includes generating clipped histograms at the different scale resolutions based on the downsampled input frame. The method also includes generating lookup tables (LUTs) at the different scale resolutions based on the texture maps and the clipped histograms. The method further includes applying the LUTs at the different scale resolutions to the input frame in order to generate locally contrast-enhanced frames. In addition, the method includes blending the locally contrast-enhanced frames and the globally contrast-enhanced frame to generate an output frame.