High Dynamic Range Image Processing via Weight Distribution Blending
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Solution Overview
Problem
Existing imaging technologies struggle to capture high dynamic range scenes effectively, as typical digital cameras with limited dynamic range often saturate bright areas and lose details in dark regions, and most displays or printers cannot produce high dynamic range outputs.
Innovation Solution
A method to generate a low dynamic range image from a high dynamic range image by determining regions with extreme pixel values, computing a weight distribution, and blending images from different exposure settings to preserve details in both highlight and shadow regions, allowing for display or printing on low dynamic range devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a digital camera uses a given exposure setting to capture an image, then the image can be taken quickly and easily, but the bright areas become saturated and details are lost
Solution Approach 1:
The image capture process is segmented into multiple exposures of the same scene at different exposure durations. Instead of capturing one image, the system captures multiple images with varying exposure times, allowing different portions of the dynamic range to be recorded in each exposure. This segmentation enables recovery of detail information from both bright and dark regions.
Solution Approach 2:
Multiple images are captured in advance with different exposure settings before processing. By taking multiple preliminary images at different exposures, the system prepares all necessary data to reconstruct the high dynamic range scene, ensuring that detail information is preserved before the final image generation.
2Loss of information
If multiple images with different exposure durations are taken to recover high dynamic range, then detail information is improved, but the processing complexity increases
Solution Approach 1:
The system changes the exposure duration parameter across multiple images to capture different dynamic range portions. By systematically varying this parameter, the patent creates a set of images that collectively represent the full dynamic range, which can then be processed to recover detailed information.
Solution Approach 2:
Multiple copies of the same scene are captured with different exposure settings. These copies serve as source material for reconstructing the high dynamic range image, allowing the system to select and combine the best-exposed portions from each copy to create the final image with preserved details.
3Loss of information
If a high dynamic range image is generated to preserve all scene details, then image quality is improved, but compatibility with standard displays is lost
Solution Approach 1:
The patent applies local quality by selectively mapping different portions of the high dynamic range image to different tonal ranges in the low dynamic range output. Bright regions, mid-tone regions, and shadow regions are handled differently through selective mapping functions, allowing each region to be optimized for detail preservation while adapting to the limited dynamic range of standard displays.
Solution Approach 2:
The final low dynamic range image is created as a composite by combining selectively mapped portions from the high dynamic range image. Different regions of the image are processed through different mapping functions and then composited together, creating a unified image that preserves details from various dynamic range portions while being compatible with standard displays.
Data Source
AI summary
Methods and apparatuses for generating a low dynamic range image for a high dynamic range scene. In one aspect, a method to generate a low dynamic range image from a high dynamic range image, includes: determining one or more regions of the high dynamic range image containing pixels having values that are outside a first range and inside a second range; computing a weight distribution from the one or more regions; and generating the low dynamic range image from the high dynamic range image using the weight distribution. In another aspect, a method of image processing, includes: detecting one or more regions in a first image of a high dynamic range scene according to a threshold to generate a mask; and blending the first image and a second image of the scene to generate a third image using the mask.