Camera Image Processing for Green Ghost Artifact Detection
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Solution Overview
Problem
Cameras in integrated computing devices, such as smartphones and tablets, face constraints that lead to unwanted green ghost artifacts due to light scattering and reflection within the optical elements and image sensor, deteriorating image quality and obscuring actual light sources.
Innovation Solution
Implementing classification techniques using keypoint generation-based, mask image generation-based, and temporal filtering-based classifiers to detect green ghost artifacts, followed by an artifact mitigation engine to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If cameras are integrated into computing devices with constraints on processing power, thermal output, and physical size, then device portability and integration are improved, but image quality deteriorates due to unwanted green ghost artifacts
Solution Approach 1:
The system performs preliminary detection of green ghost artifacts using multiple classification techniques (keypoint generation-based, mask image generation-based, and temporal filtering-based classifiers) before final image output. This allows the artifact mitigation engine to identify and remove ghosts in advance, preventing them from degrading image quality in the final captured image.
Solution Approach 2:
An artifact mitigation engine is introduced as an intermediary component between the image sensor and the final image output. This engine receives images from the constrained integrated camera, processes them through multiple classification techniques, and produces corrected images with green ghost artifacts removed, thereby mediating the harmful effect without requiring changes to the physical camera hardware.
2Measurement precision
If multiple classification techniques are applied to detect green ghost artifacts, then detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The detection process is segmented into three distinct classification techniques, each handling different aspects of artifact detection: keypoint generation-based for spatial pattern recognition, mask image generation-based for region identification, and temporal filtering-based for motion analysis. This segmentation allows each technique to be optimized independently and enables parallel processing, reducing overall computational time while maintaining high detection accuracy.
Solution Approach 2:
The system dynamically adjusts processing parameters based on image characteristics and computational resources available. The artifact mitigation engine can modify the sensitivity thresholds, processing resolution, and algorithm selection based on the specific image content and device constraints, optimizing the balance between detection accuracy and processing time for each individual image.
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
Effectively detects and mitigates green ghost artifacts, enhancing image quality by accurately identifying and removing these artifacts from captured images.
Implementation Method 1
an image sensor
Implementation Method 2
light scattering and reflection within the optical elements and image sensor
Implementation Method 3
light scattering and reflection within the optical elements and image sensor
Data Source
AI summary
Disclosed herein are a system, method, and computer program product embodiments for detecting green ghost artifacts utilizing different classification techniques. One technique utilizes a keypoint generation-based classifier, where keypoint(s) corresponding to green ghost artifact(s) are generated in a region of an image that likely includes such artifacts. Another technique utilizes a mask image generation-based classifier, where a mask image is generated that includes confidence level(s) for pixel(s) that indicate a likelihood that such pixel(s) include such artifacts. A further technique utilizes a temporal filtering-based classifier, where a patch distance between a patch of pixels of the image and a patch of pixels of a corresponding history image is determined. The patch distance indicates a likelihood that such artifacts are present in the image. The output of such classifiers are utilized collectively to determine whether such artifacts are present in the image.