Dynamic Enhancement Factor Fusion for High Resolution Image Generation
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Solution Overview
Problem
Conventional image processing systems are static in selecting enhancement factors for low resolution images, making them unsuitable for all images, as the enhancement factor does not dynamically adapt to the characteristics of each image.
Innovation Solution
A processor-implemented method and system that collect and pre-process multiple low resolution images by changing their characteristics relative to a reference image, dynamically determining final enhancement factors to enhance and fuse them into a high resolution image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static enhancement factor is used for image processing, then the system is simple to operate, but the system is not suitable for all low resolution images depending on their characteristics
Solution Approach 1:
The patent applies dynamics by transitioning from a static enhancement factor to a dynamic selection mechanism. The system now adapts the enhancement factor based on image characteristics such as blur level, noise, and content type. This is achieved through automated analysis of each input image to determine the appropriate enhancement parameters, making the system flexible and suitable for diverse image types while maintaining ease of operation through automation.
Solution Approach 2:
The patent implements parameter changes by modifying the enhancement factor based on detected image characteristics. The system analyzes parameters such as image blur, noise levels, and content type, then dynamically adjusts the enhancement factor accordingly. This allows the same system to optimize processing for different image types (e.g., portraits, landscapes, documents) without requiring manual intervention or complex user configuration.
2Adaptability or versatility
If multiple processing parameters are adjusted for different image characteristics, then the suitability for various images is improved, but the processing complexity increases
Solution Approach 1:
The patent applies self-service by enabling the image processing system to automatically analyze and determine its own processing parameters. The system independently evaluates image characteristics such as blur, noise, and content type, then selects appropriate enhancement factors without external intervention. This automation handles the complexity internally while presenting a simple interface to users, making the system adaptable to various image types without increasing operational complexity.
Solution Approach 2:
The patent implements feedback mechanisms by continuously analyzing image characteristics and adjusting processing parameters based on the results. The system monitors image quality metrics during processing and dynamically modifies enhancement factors to optimize output. This closed-loop approach allows the system to adapt to different image types automatically, managing processing complexity through intelligent feedback-driven parameter adjustment rather than requiring manual configuration.
Data Source
AI summary
This disclosure relates generally to image processing, and more particularly to generate a high resolution image from multiple low resolution images. In an embodiment, the system collects a plurality of low resolution images as input, processes the collected images, and generates a high resolution image as output. During this process, the system pre-processes the collected low resolution images, and during this process, at least one characteristic of each of the low resolution image is changed with respect to a reference image. After the pre-processing stage, the images are then enhanced based on a plurality of final enhancement factors that are dynamically determined. Further, the enhanced images are fused to generate the high resolution image.