HDR Composite Imaging with Multi-Threshold Motion Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing high dynamic range (HDR) image generation techniques face challenges in balancing ghosting and noise artifacts due to the use of fixed motion thresholds, which can lead to suboptimal composite images with either excessive ghosting or noise.
Innovation Solution
A system and method for generating composite images using multiple motion thresholds to compare and select regions from composite images generated with different motion thresholds, employing a structural-similarity algorithm to determine the inclusion of regions based on similarity scores, thereby reducing ghosting and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed motion threshold is used in HDR image generation, then the processing complexity is reduced, but the image quality deteriorates due to excessive ghosting or noise
Solution Approach 1:
The patent divides the image processing into multiple stages: generating multiple composite images with different motion thresholds, comparing them through structural similarity, and selectively combining regions. This segmentation allows each stage to optimize for specific quality aspects while managing complexity through modular processing
Solution Approach 2:
The patent varies the motion threshold parameter across multiple composite image generations (e.g., first motion threshold, second motion threshold). By changing this parameter and comparing results through structural similarity metrics, the system optimizes image quality without requiring excessive computational complexity
2Manufacturing precision
If multiple motion thresholds are used to generate composite images, then image quality improves by reducing ghosting and noise, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by generating multiple composite images with different motion thresholds before final comparison. This allows the system to pre-compute candidate solutions and then efficiently select the best regions through structural similarity comparison, reducing overall processing time
Solution Approach 2:
The patent applies different motion thresholds to different regions or generates multiple composite images where each may be optimal for different areas. The structural similarity comparison then selectively combines regions from different composite images, ensuring local optimization without requiring global reprocessing
3Productivity
If a single composite image is generated with a fixed motion threshold, then the processing speed is maintained, but the image quality deteriorates with ghosting and noise artifacts
Solution Approach 1:
The patent merges multiple composite images generated with different motion thresholds by comparing structural similarity and selectively combining regions. This combining approach maintains processing efficiency while achieving superior image quality by leveraging the strengths of multiple threshold-based composites
Data Source
AI summary
Systems and techniques are described herein for generating composite image data. For instance, a method for generating composite image data is provided. The method may include generating a first composite image based on a first image, a second image, and a first motion threshold; generating a second composite image based on the first image, the second image, and a second motion threshold; comparing a region of the first composite image with a region of the second composite image; and outputting image data based on the comparison.