Image Enhancement via Local Artifact-Adaptive Processing
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Solution Overview
Problem
Conventional image processing techniques fail to provide sufficient image clarity in degraded visibility environments due to factors like fog, haze, sand-brownouts, smoke, rain, and snow, making it difficult to use image sequence data effectively.
Innovation Solution
An image enhancing apparatus and method that adjusts local area processing based on the level of environmental artifacts by determining background and foreground optical flow motions, adjusting parameters such as noise and histogram thresholds, and applying wavelet-based image processing to enhance image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques are used, then the processing is simple and fast, but the image clarity is insufficient in degraded visibility environments
Solution Approach 1:
The image is divided into multiple local areas, and processing parameters are determined separately for each local area based on its specific environmental artifact level. This allows different regions to be processed with appropriate complexity - simple regions use basic processing while complex regions with severe artifacts use enhanced processing, thus improving overall image clarity without uniformly increasing processing complexity across the entire image.
Solution Approach 2:
The processing parameters (such as noise threshold, contrast enhancement strength) are dynamically adjusted based on the detected level of environmental artifacts in each local area. The system automatically adapts the processing intensity to match the actual degradation level, providing stronger processing only where needed and maintaining simplicity in clearer regions, thereby resolving the contradiction between image clarity and processing complexity.
2Measurement precision
If local area processing is applied to all regions, then image clarity improves, but processing time and computational load increase
Solution Approach 1:
Different processing strategies are applied to different local areas based on their specific characteristics. Regions with severe environmental artifacts receive intensive local area processing to improve clarity, while regions with minimal artifacts use simplified or no processing. This selective approach ensures image clarity is improved where necessary without incurring the full computational cost of processing the entire image at high detail.
Solution Approach 2:
The system applies processing at varying levels of intensity across different regions rather than uniformly applying maximum processing everywhere. By using partial action (reduced processing in good regions) and excessive action (enhanced processing in degraded regions), the system achieves adequate image clarity overall while significantly reducing total processing time compared to applying full-strength processing to the entire image.
3Reliability
If processing parameters are adjusted for each local area, then image quality in degraded regions improves, but device complexity increases
Solution Approach 1:
The system automatically detects environmental artifacts and determines appropriate processing parameters for each local area without requiring manual configuration or complex external control systems. The apparatus self-adjusts by analyzing the image content itself, identifying regions with fog, rain, or other artifacts, and applying suitable processing parameters, thereby improving image quality while keeping the control system relatively simple.
Solution Approach 2:
The system manages complexity by systematically varying a limited set of processing parameters (such as noise threshold, contrast gain, sharpness) across different local areas rather than introducing entirely different processing algorithms for each region. This parameter-based approach allows flexible adaptation to different degradation levels while maintaining a unified processing framework, thus improving image quality without proportionally increasing system complexity.
Data Source
AI summary
Described herein is a method of enhancing an image includes determining a level of environmental artifacts at a plurality of positions on an image frame of image data. The method also includes adjusting local area processing of the image frame, to generate an adjusted image frame of image data, based on the level of environmental artifacts at each position of the plurality of positions. The method includes displaying the adjusted image frame.