HDR Image Processing via Interlaced Field Separation and Blending
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Solution Overview
Problem
Existing methods for generating High Dynamic Range (HDR) images from interlaced sensors result in a loss of spatial density, as they typically involve capturing exposure-bracketed images sequentially, which can lead to ghost artifacts and reduced detail in areas with different exposure levels.
Innovation Solution
A method that separates and upscales interlaced fields from an HDR sensor, blends them to generate a high-dynamic range image, identifies and removes ghost artifacts, and modifies detail areas using the original image to maintain spatial density and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If exposure-bracketed images are captured sequentially, then high dynamic range is achieved, but spatial density is lost and ghost artifacts appear
Solution Approach 1:
The patent divides the interlaced sensor data into separate odd and even fields, processing each field independently through upsampling and blending operations. This segmentation allows preservation of spatial density while achieving HDR by treating each interlaced field as a separate exposure layer that can be processed without the ghosting issues of sequential capture.
2Manufacturing precision
If interlaced fields are processed separately, then spatial density is improved, but processing complexity increases
Solution Approach 1:
The patent merges the separately processed odd and even fields through blending operations to create the final HDR image. By upsampling each field to match resolution and then blending them with appropriate weighting, the method achieves high spatial density while managing processing complexity through systematic combination of processed fields.
3Object-generated harmful factors
If ghost artifacts are removed, then image quality is improved, but detail information may be lost
Solution Approach 1:
The patent converts the potential harm of ghost artifacts into a benefit by using the interlaced field structure itself. Instead of treating ghosting as a problem to be eliminated through aggressive artifact removal, the method uses the temporal separation inherent in interlaced fields to create distinct exposure layers that, when blended, naturally suppress ghosts while preserving detail through the complementary information in each field.
Data Source
AI summary
A method, apparatus, and manufacture for generating an HDR image is provided. An original image is received from an HDR interlaced sensor that includes at least two fields captured with different exposures. The fields are separated from each other to provide separate images, and each of the separate images is upscaled. Next, blending is performed on each of the upscaled separate images to generate a high-dynamic range image, and ghost identification is performed on the high-dynamic range image. Subsequently, detail identification is performed on the high-dynamic range image. The detail identification includes identifying areas in the non-ghost areas of the high-dynamic range image that have details, and modifying the high-dynamic image by replacing each of the areas identified to have details with the corresponding area from the original image.