Dynamic TET Sequence for HDR Image Detail Preservation
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Solution Overview
Problem
Low dynamic range (LDR) imaging struggles to capture scenes with wide brightness variations, often resulting in loss of detail in both bright and dark regions due to the limitations of 8-bit pixel brightness representation, where a single total exposure time (TET) cannot adequately expose both extremes.
Innovation Solution
Capturing multiple images of a scene with different total exposure times (TETs) and digitally processing these images to construct an output image that combines the details from both bright and dark regions, using techniques such as histogram analysis and tonemapping to determine optimal TET sequences for HDR imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single total exposure time (TET) is used to capture an image, then the capture process is simple and fast, but the image cannot preserve details in both bright and dark regions of high dynamic range scenes
Solution Approach 1:
The patent divides the image capture process into multiple segments by capturing multiple images with different TETs (short TET for bright regions, long TET for dark regions). This segmentation allows each region to be captured under optimal exposure conditions, resolving the contradiction between fast capture and detailed brightness representation.
Solution Approach 2:
The patent dynamically adjusts the TET parameter across different images based on the scene's lighting conditions. By varying TET from short to long depending on the region being captured, the system adapts to preserve details in both bright and dark areas while maintaining overall capture efficiency.
2Measurement precision
If multiple images with different TETs are captured to preserve brightness details, then the brightness dynamic range is improved, but the capture process becomes more complex and time-consuming
Solution Approach 1:
The patent performs preliminary actions by capturing a first plurality of images with different TETs before the main capture process. This preliminary step determines the optimal TET sequence for subsequent image capture, simplifying the overall process by pre-calculating exposure parameters based on scene analysis.
Solution Approach 2:
The patent systematically changes the TET parameter across multiple images to capture different brightness ranges. By varying TET as a key parameter and using histogram analysis to determine optimal values, the system achieves high dynamic range without excessive complexity in the capture process.
3Manufacturing precision
If multiple images with different TETs are captured and processed, then the output image quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary histogram analysis and TET sequence determination before the main image capture and processing. By pre-analyzing the scene and determining optimal TET sequences in advance, the system reduces processing time during the actual image capture and reconstruction phases.
Solution Approach 2:
The patent maintains continuous useful action by seamlessly integrating the multiple TET captures and processing them into a unified output image. The continuous workflow from capture to reconstruction without idle periods minimizes total processing time while maintaining high image quality.
Data Source
AI summary
A first plurality of images of a scene may be captured. Each image of the first plurality of images may be captured using a different TET. Based at least on the first plurality of images, a long TET, a short TET, and a TET sequence that includes the long TET and the short TET may be determined. A second plurality of images of the scene may be captured. The images in the second plurality of images may be captured sequentially in an image sequence using a sequence of TETs corresponding to the TET sequence. Based on one or more images in the image sequence, an output image may be constructed.