Adaptive Image Enhancement for Low-Visibility Object Analysis
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Solution Overview
Problem
Conventional image enhancement algorithms fail to adapt dynamically to image analysis models, leading to issues like overexposure and noise amplification in low-visibility conditions, particularly in nighttime scenes with light sources, and uniform enhancement methods are ineffective for objects resembling the background.
Innovation Solution
An image analysis system that employs a pre-trained parameter extraction model to perform pixel-wise enhancement using parameter arrays, adapted through end-to-end training with an image analysis model, incorporating operations like denoising, contrast adjustment, brightness adjustment, and sharpening, to enhance images dynamically and improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional image enhancement algorithms (histogram equalization, gamma correction) are applied to enhance brightness, then image brightness is improved, but image noise is amplified and overexposure occurs near light sources
Solution Approach 1:
The patent applies different enhancement strategies to different regions of the image. Dark regions undergo brightness enhancement while light source regions are protected from overexposure through region-specific processing. This local differentiation resolves the contradiction by enabling brightness improvement without uniform overexposure across the entire image.
Solution Approach 2:
The patent segments the image into dark regions and light source regions, applying separate enhancement functions to each. This segmentation allows the system to enhance brightness where needed while avoiding the harmful effects in light source areas, thus resolving the contradiction between brightness improvement and noise amplification.
2Adaptability or versatility
If maximum entropy threshold segmentation is used to separate bright and dark regions, then regional enhancement is achieved, but the enhanced image appears unsmooth and parameters are not adapted to image analysis model needs
Solution Approach 1:
The patent employs dynamic threshold segmentation where the threshold value is not fixed but adapts based on image characteristics and model requirements. This dynamic adjustment allows the system to maintain regional enhancement capability while ensuring image smoothness and parameter adaptation to specific image analysis tasks.
Solution Approach 2:
The patent incorporates feedback mechanisms where the enhancement parameters are adjusted based on the output of image analysis models. This feedback loop ensures that the enhanced image maintains smoothness and that parameters are optimized for the specific needs of the image analysis task, resolving the contradiction between regional enhancement and parameter adaptation.
3Adaptability or versatility
If deep learning methods extract features and apply filters uniformly, then feature-based enhancement is achieved, but parameters do not actively adapt to image analysis model needs and noise is amplified
Solution Approach 1:
The patent transitions from uniform filter application to local quality enhancement by applying different enhancement operations to different regions. This local differentiation ensures that enhancement parameters actively adapt to the specific needs of the image analysis model while preventing noise amplification through region-specific processing.
Solution Approach 2:
The patent implements dynamic parameter adjustment where enhancement filters are not static but adapt based on extracted features and model requirements. This dynamic approach allows the system to maintain feature-based enhancement while ensuring parameters actively adapt to image analysis model needs and control noise amplification.
Data Source
AI summary
An image analysis method is provided. The method includes inputting an original image into a parameter extraction model having been pre-trained to obtain multiple parameter arrays corresponding to the original image, which are output by the parameter extraction model. The method further includes using the parameter arrays to perform a sequence of image enhancement operations on the original image to obtain an enhanced image. The method further includes inputting the enhanced image into an image analysis model having been pre-trained to obtain an inference result output by the image analysis model.