Image Processing System SDR to HDR Conversion via Neural Network Optimization
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Solution Overview
Problem
Environmental conditions such as insufficient lighting and variations in image capturing and display equipment specifications lead to poor image quality, with existing technologies failing to effectively enhance images across different lighting conditions and device specifications.
Innovation Solution
A method and system that process images by applying a pixel/image relationship to adjust pixel characteristics, utilizing a convolutional neural network trained with image quality loss processes to enhance images, particularly converting standard dynamic range (SDR) signals to high dynamic range (HDR) signals, improving exposure, contrast, and color consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If image processing is performed to enhance quality in low-light conditions, then visibility and brightness are improved, but processing complexity and computational resources increase
Solution Approach 1:
The patent transforms the image enhancement problem into a parameter optimization problem by defining a loss function that quantifies image quality. The system adjusts processing parameters iteratively to minimize this loss function, thereby improving brightness and visibility while managing computational complexity through mathematical optimization rather than brute-force processing.
Solution Approach 2:
The system performs self-optimization by automatically adjusting processing parameters based on the loss function evaluation. The image processing pipeline adapts to different lighting conditions and device specifications autonomously, reducing the need for manual intervention and complex pre-programming of processing algorithms.
2Adaptability or versatility
If image processing adapts to different device specifications, then compatibility and display quality are improved, but processing time and computational load increase
Solution Approach 1:
The system handles device specification variations by adjusting processing parameters based on device capabilities. The loss function incorporates device-specific characteristics, allowing the same processing framework to adapt to different sensors, displays, and environmental conditions without requiring separate processing pipelines for each device type.
Solution Approach 2:
The patent creates a universal image processing framework that can handle multiple device specifications and environmental conditions through a single unified approach. The loss function and optimization process are designed to be device-agnostic, accommodating various sensors, displays, and lighting conditions without requiring device-specific algorithm development.
3Manufacturing precision
If pixel characteristics are adjusted to enhance dynamic range, then image quality and detail visibility are improved, but processing complexity and computational resources increase
Solution Approach 1:
The system optimizes pixel characteristics by treating them as adjustable parameters in the loss function minimization process. Instead of complex manual tuning of each pixel parameter, the system automatically adjusts exposure, contrast, and color parameters through gradient-based optimization, significantly reducing processing complexity while maintaining high image quality.
Solution Approach 2:
The loss function provides continuous feedback on image quality during processing, allowing the system to iteratively refine pixel characteristics. This feedback mechanism enables automatic optimization of dynamic range and detail visibility without requiring complex pre-programmed processing rules or extensive manual parameter adjustment.
Data Source
AI summary
A system and method for processing an image including the steps of receiving an input image having a plurality of pixels, wherein each of the plurality of pixels have one or more pixel characteristics; and processing the input image to generate an enhanced image by applying a pixel/image relationship to each of the plurality of pixels of the input image, wherein the pixel/image relationship is arranged to adjust the one or more pixel characteristics of each of the plurality of pixels of the input image.