Dual Image Sensor Processing System for Defective Pixel Correction
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Solution Overview
Problem
Conventional digital imaging devices face issues with defective pixels, lens shading irregularities, and inadequate demosaicing techniques, leading to artifacts and noise in processed images, particularly due to manufacturing defects and operational failures.
Innovation Solution
The system employs advanced image processing techniques, including defective pixel detection and correction, lens shading correction, and improved demosaicing methods that account for edge locations and directions, using a front-end pixel processing unit that operates in single or dual sensor modes, and applies temporal filtering and binning compensation to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional demosaicing techniques interpolate values using fixed thresholds, then processing speed is maintained, but edge artifacts such as aliasing and rainbow artifacts are introduced
Solution Approach 1:
The patent applies dynamics by transitioning from fixed threshold interpolation to adaptive threshold selection based on local image characteristics. The system dynamically determines interpolation thresholds according to edge directions and local variance, allowing the demosaicing process to adapt to different regions of the image. This resolves the contradiction by maintaining processing efficiency while significantly reducing edge artifacts through context-aware interpolation.
Solution Approach 2:
The patent implements local quality by applying different interpolation strategies to different regions of the image based on local characteristics such as edge orientation and variance. Instead of using a uniform approach, the system identifies edge regions versus non-edge regions and applies appropriate interpolation methods to each, thereby improving image quality in critical areas while maintaining overall processing efficiency.
2Manufacturing precision
If conventional sharpening techniques are applied to enhance image detail, then edge definition is improved, but noise is amplified and indistinguishable from actual image features
Solution Approach 1:
The patent applies local quality by implementing region-specific sharpening operations that consider local image characteristics. The system identifies edge regions and applies sharpening primarily to these areas while using different or reduced sharpening strength in non-edge regions. This selective approach enhances edge definition where needed while minimizing noise amplification in uniform areas, resolving the contradiction between sharpness and noise.
Solution Approach 2:
The patent implements dynamics by making the sharpening operation adaptive to local image content. The sharpening strength and application are dynamically adjusted based on local variance and edge detection results, allowing the system to enhance edges in high-contrast regions while avoiding excessive sharpening in low-contrast or noisy regions, thereby balancing edge definition improvement with noise control.
3Manufacturing precision
If dual sensor mode is used to improve image quality through additional data, then image uniformity and defect correction are enhanced, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies merging by combining data from multiple sensors through an interleaved processing architecture. The system merges image frames from first and second sensors in an alternating pattern, allowing efficient utilization of dual sensor data while sharing processing resources. This approach enhances image uniformity and defect correction capabilities while managing system complexity through resource sharing and integrated processing pipelines.
Solution Approach 2:
The patent implements segmentation by dividing the processing of dual sensor data into separate interleaved streams. The system processes frames from each sensor independently in alternating sequence, allowing modular processing that manages complexity while leveraging the benefits of multiple sensors. This segmented approach enables efficient defect correction and uniformity improvement without requiring all sensors to be processed simultaneously, thus managing system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively reduces artifacts and noise, improves light intensity uniformity, and produces sharper images with reduced edge artifacts, resulting in higher image quality and viewer satisfaction.
Implementation Method 1
an image sensor that provides a number of light-detecting elements (e.g., photodetectors) configured to convert light detected by the image sensor into an electrical signal
Data Source
AI summary
Various techniques are provided for processing image data acquired using a digital image sensor. In accordance with aspects of the present disclosure, one such technique may relate to the processing of image data in a system that supports multiple image sensors. In one embodiment, the image processing system may include control circuitry configured to determine whether a device is operating in a single sensor mode (one active sensor) or a dual sensor mode (two active sensors). When operating in the single sensor mode, data may be provided directly to a front-end pixel processing unit from the sensor interface of the active sensor. When operating in a dual sensor mode, the image frames from the first and second sensors are provided to the front-end pixel processing unit in an interleaved manner. For instance, in one embodiment, the image frames from the first and second sensors are written to a memory, and then read out to the front-end pixel processing unit in an interleaved manner.