Bayer-Clear Image Fusion for Dual Camera Noise Reduction
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Solution Overview
Problem
Dual camera systems face challenges in generating high-quality images due to inherent differences in perspective and unsynchronized captures, leading to errors and distortion, especially when using multiple image sensors with different capabilities and capacities.
Innovation Solution
The implementation of image fusion techniques on Bayer and Clear image data, where both sensors are synchronized to capture images, generating a relational mapping between luma channel data to merge de-noised representations and reduce artifacts, resulting in improved signal-to-noise ratio and reduced distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual camera systems are used to improve image quality, then picture quality is enhanced, but device size increases
Solution Approach 1:
The patent combines data from multiple image sensors (Bayer sensor and Clear sensor) through image fusion techniques to produce a single high-quality image. By merging the color information from the Bayer sensor with the high-dynamic-range and low-noise information from the Clear sensor, the system achieves superior image quality without requiring each individual sensor to be large or complex.
Solution Approach 2:
The patent divides the image capture function across two specialized sensors: a Bayer sensor for color information and a Clear sensor for luminance and dynamic range. Each sensor is optimized for its specific function, allowing smaller, more specialized components to work together rather than requiring a single large sensor to handle all functions.
2Measurement precision
If multiple image sensors are used to capture images, then image quality is improved, but perspective differences and synchronization errors cause distortion
Solution Approach 1:
The patent introduces an image fusion module as an intermediary that receives data from both sensors and processes them together. This fusion module applies sophisticated algorithms to align the images, correct perspective differences, and merge the data streams, acting as a mediator that resolves the alignment and synchronization issues between the two sensors.
Solution Approach 2:
The patent employs feedback mechanisms in the form of image fusion algorithms that continuously adjust and refine the alignment and merging of images from both sensors. The system analyzes the captured data, identifies misalignments and distortions, and applies corrective transformations to produce a unified, high-quality image, creating a closed-loop system that self-corrects alignment errors.
Data Source
AI summary
The embodiments described herein perform image infusion on Bayer image data that is coordinated with Clear image data captured with a dual camera. A first image sensor of the dual camera captures a Bayer image, while a second sensor captures a Clear image, where the first sensor and the second sensor are synchronized to capture the images at the same time. Some embodiments generate a relational mapping of Clear luma channel data extracted from the Clear image to Bayer luma channel data extracted from the Bayer image data, and use the relational mapping to generate multiple de-noised luma representations associated with the Bayer image. The multiple de-noised luma representations are then selectively merged together to generate a resultant image that retains a high signal-to-noise radio (SNR) and reduces artifacts obtained by the image processing.