High Dynamic Range Image Processing via Saturation-Based Region Segmentation
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Solution Overview
Problem
Current digital image and video processing technologies face challenges in effectively reducing noise and capturing high dynamic range images, particularly in scenarios with varying exposure times and overlapping fields of view, which affects image quality and dynamic range representation.
Innovation Solution
The implementation of an image processing system that combines current frames with recirculated frames using mixing weights, determines noise maps based on noise levels, and applies motion compensation to improve noise reduction and dynamic range imaging, utilizing an image signal processor to handle multiple exposure times and overlapping fields of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple images with different exposure times are combined to determine a high dynamic range image, then the dynamic range representation is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the image processing task by dividing the combined image into multiple regions based on saturation detection. Each region is then processed differently - oversaturated regions use short exposure data while undersaturated regions use long exposure data. This segmentation approach resolves the contradiction by enabling complex HDR processing through systematic region-based decomposition.
Solution Approach 2:
The patent applies local quality by using different processing strategies for different regions of the image. Specifically, it detects saturation levels in each region and applies appropriate exposure time data locally - short exposure for bright/oversaturated regions and long exposure for dark/undersaturated regions. This local differentiation achieves high dynamic range representation while managing processing complexity through region-specific optimization.
2Reliability
If frames are recirculated and combined using mixing weights to reduce noise, then the noise reduction is improved, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating mixing weights based on noise level estimates before combining frames. The noise levels are estimated in advance for each frame, and these estimates are used to determine optimal mixing weights that will be applied during frame combination. This preliminary preparation reduces the computational burden during actual processing, thereby reducing processing time while maintaining noise reduction quality.
Solution Approach 2:
The patent implements feedback by using noise level estimates from previously processed frames to inform the mixing weight calculation for current frame combination. The recirculated frames provide feedback about noise characteristics, which are then used to optimize the combination process. This feedback mechanism improves noise reduction by continuously adapting mixing weights based on observed noise patterns while managing processing time through efficient feedback utilization.
3Manufacturing precision
If motion compensation is applied to recirculated frames, then the image quality is improved, but the computational load increases
Solution Approach 1:
The patent applies partial action by selectively applying motion compensation only to recirculated frames that contribute to the final output, rather than processing all frames uniformly. The mixing weights determine which recirculated frames are actually used, and motion compensation is applied only where needed. This partial application of motion compensation maintains image quality in regions requiring it while reducing computational load in regions where recirculated frames are not used.
Data Source
AI summary
Systems and methods are disclosed for image signal processing. For example, systems may include an image sensor and a processing apparatus. The image sensor captures image data using a plurality of selectable exposure times. The processing apparatus receives a first image from the image sensor captured with a first exposure time and receives a second image from the image sensor captured with a second exposure time that is less than the first exposure time. A high dynamic range image is determined based on the first image and the second image, wherein an image portion of the high dynamic range image is based on a corresponding image portion of the second image when a pixel of a corresponding image portion of the first image is saturated. An output image that is based on the high dynamic range image is stored, displayed, or transmitted.