High Dynamic Range Image Combining via Tone Mapping
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Solution Overview
Problem
Current technologies face challenges in combining high dynamic range images due to compatibility issues with lower dynamic range image transport and display mechanisms, as high dynamic range images require more data than supported by many displays and transport standards.
Innovation Solution
The method involves receiving image data from an image sensor using different exposure times, scaling luminance values based on exposure time ratios, selectively combining the data to generate a high dynamic range image, and applying tone mapping to compress the dynamic range, using both global and local tone mapping techniques to achieve compatibility with lower dynamic range systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high dynamic range image data is captured and processed, then image quality and contrast are improved, but compatibility with display and transport standards deteriorates
Solution Approach 1:
The patent applies tone mapping to transform luminance values from high dynamic range to low dynamic range. This involves changing the parameter space of luminance values through mathematical transformations (such as piecewise linear mapping, exponential mapping, or logarithmic mapping) to compress the dynamic range while preserving image quality and compatibility with display standards.
Solution Approach 2:
The patent introduces tone mapping as an intermediary processing step between HDR image capture and display. This intermediary transformation layer converts the high dynamic range data into a format compatible with standard displays, acting as a mediator that bridges the gap between HDR capabilities and LDR constraints.
2Measurement precision
If multiple exposure images are combined to create HDR image, then dynamic range is improved, but data requirements and processing complexity increase
Solution Approach 1:
The patent segments the HDR image processing into distinct functional modules: image capture with multiple exposures, image alignment and registration, luminance scaling, selective combining based on effective dynamic range overlap, and tone mapping. This segmentation allows each module to be optimized independently and simplifies the overall processing pipeline.
Solution Approach 2:
The patent applies local tone mapping techniques where different regions of the image receive different processing treatments based on their luminance characteristics. This allows optimization of processing complexity for each region, applying more aggressive compression where needed and preserving detail where important, thereby reducing overall processing burden while maintaining quality.
3Adaptability or versatility
If tone mapping is applied to compress dynamic range, then compatibility with display standards is improved, but image detail and contrast may be lost
Solution Approach 1:
The patent employs sophisticated parameter transformation techniques in tone mapping, including piecewise linear mapping with multiple segments, exponential mapping for preserving highlights, and logarithmic mapping for compressing dark regions. These parameter changes are designed to maintain image detail and contrast while achieving compatibility with display standards.
Solution Approach 2:
The patent implements adaptive tone mapping where the mapping parameters are dynamically adjusted based on image content characteristics, histogram analysis, and luminance distribution. This dynamic adaptation allows the system to optimize the balance between compression and detail preservation for each specific image, rather than using a fixed transformation.
Data Source
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AI summary
Systems and methods of high dynamic range image combining are disclosed. In a particular embodiment, a device includes a global mapping module configured to generate first globally mapped luminance values within a region of an image, a local mapping module configured to generate second locally mapped luminance values within the region of the image, and a combination module configured to determine luminance values within a corresponding region of an output image using a weighted sum of the first globally mapped luminance values and the second locally mapped luminance values. A weight of the weighted sum is at least partially based on a luminance variation within the region of the image.