CMOS Rolling Shutter Flicker Detection via Column Luminance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital camera systems face challenges in reliably detecting flicker caused by artificial light sources, particularly due to the rolling shutter effect in CMOS sensors, leading to unwanted variability and noticeable horizontal bars in images, which existing methods struggle to address efficiently.
Innovation Solution
A system and method for flicker detection that involves dividing images into columns and performing a weighted sum of pixel luminance using alternating sign weights, comparing frames to identify simultaneous luminance variations, and adjusting camera parameters to compensate for detected flicker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rolling shutter is used in CMOS sensor for sequential image acquisition, then device complexity is reduced and manufacturing cost is lowered, but flicker artifacts appear in images due to interaction with alternating current modulated light sources
Solution Approach 1:
The patent implements a feedback mechanism by detecting flicker in captured images and automatically adjusting the shutter speed to eliminate the artifact. The system analyzes image data for flicker patterns, determines the frequency of light source modulation, and modifies the rolling shutter timing accordingly to synchronize with the light source cycle, thereby preventing flicker artifacts from appearing in subsequent images.
Solution Approach 2:
The patent changes the operational parameters of the rolling shutter by dynamically adjusting the exposure time and row readout timing based on detected flicker frequency. When flicker is detected, the system modifies the shutter speed and timing parameters to match the alternating current frequency, transforming the fixed rolling shutter operation into an adaptive operation that eliminates flicker while maintaining the cost-effective CMOS sensor architecture.
2Reliability
If existing flicker detection methods are used, then flicker detection capability is provided, but reliability is limited and computing resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential information needed for flicker detection by analyzing specific image data characteristics rather than processing entire images. The method focuses on detecting luminance variations in corresponding rows across multiple images, extracting only the flicker-related signals while ignoring irrelevant image content, thereby reducing computing resource consumption while improving detection reliability through targeted analysis.
Solution Approach 2:
The patent segments the image data analysis by dividing the image into rows and analyzing corresponding rows across multiple images separately. This segmentation allows the system to focus computational resources on specific regions that exhibit flicker patterns, processing only the necessary data segments rather than analyzing the entire image, which reduces overall computing load while maintaining high detection reliability through systematic row-by-row comparison.
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
This approach improves the reliability and efficiency of flicker detection, reducing flicker-related image artifacts by accurately identifying horizontal patterns of flicker and allowing for effective compensation, thereby enhancing digital image quality.
Implementation Method 1
When subjected to light, the image sensor converts incident photons to electrons. The conversion enables analog electronic circuitry to process the image 'seen' by the sensor array.
Data Source
AI summary
A system and method for detecting flicker in a digital image. The system and method detects flicker by performing a frame-to-frame comparison of image data. The frame-to-frame comparison of image data is based on dividing each image into multiple columns, and performing a weighted sum of the pixel luminance in each column using a pattern of alternating sign weights. The weighted sum of luminance for each column in a first frame is compared to the weighted sum of luminance for that column in a second frame to determine if there is a simultaneous luminance variation in those columns. For example, if the difference between weighted sums of luminance between frames exceeds a threshold value in a predetermined number of columns then there is a simultaneous luminance variation in those columns, and a horizontal pattern of flicker has been detected in the image data.