Hyper-Hemispherical Image Stitching with Exposure Compensation
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Solution Overview
Problem
Existing image capture systems face challenges in handling high dynamic range scenarios, particularly when stitching images from hyper-hemispherical sensors with different exposures, as they struggle to avoid visible stitch lines and maintain image quality across varying exposure levels.
Innovation Solution
The system employs an image signal processor that obtains multiple image signals from hyper-hemispherical sensors with different exposures, applies gain values to compensate for exposure differences, and performs HDR processing on high-frequency portions of the exposure-compensated images, using techniques like edge-aware non-linear filters and local tone mapping to create a seamless and high-dynamic-range image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If images from hyper-hemispherical sensors with different exposures are stitched together, then the dynamic range of the resulting image is improved, but visible stitch lines appear at the boundaries between images
Solution Approach 1:
The patent applies different processing strategies to different regions of the stitched image. Specifically, it identifies boundary regions between hyper-hemispherical images and applies specialized blending and exposure compensation techniques only in these local areas, while preserving the full dynamic range benefits in the central regions. This localised approach allows seamless stitching without compromising the overall high dynamic range performance.
Solution Approach 2:
The patent performs exposure compensation and gain adjustment on individual hyper-hemispherical images before stitching them together. By pre-processing the images to normalize exposure levels and apply appropriate gain values, the system eliminates the need for complex post-stitching adjustments, thereby preventing visible stitch lines from forming in the first place.
2Manufacturing precision
If gain values are applied to compensate for exposure differences in stitched images, then the exposure uniformity is improved, but the processing complexity increases
Solution Approach 1:
The patent systematically adjusts exposure parameters including gain values, exposure times, and ISO settings for each hyper-hemispherical image based on its specific lighting conditions. By automating these parameter adjustments and establishing clear relationships between exposure differences and required compensation, the system achieves uniform exposure across stitched images while keeping the processing pipeline manageable through standardized algorithms.
3Manufacturing precision
If HDR processing is performed on the entire exposure compensated image, then the image quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent divides the HDR processing task into segments based on spatial frequency content. It applies full HDR processing only to high-frequency portions of the image that contain fine details and edges, while using simplified processing or direct copying for low-frequency regions that contain broader luminance variations. This selective approach maintains high image quality in critical areas while significantly reducing overall processing time and computational resource requirements.
Data Source
AI summary
Image signal processing includes obtaining two or more image signals from a first image sensor, where each of the two or more image signals has a different exposure and obtaining two or more image signals from a second image sensor, where each of the two or more image signals has a different exposure. Image signal processing includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. Image signal processing further includes processing a transformed base layer of the exposure compensated image to obtain a high dynamic range (HDR) image.