Under-Display Camera Frame Correction for Starburst and Glare
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Solution Overview
Problem
The under-display camera (UDC) structure in electronic devices experiences optical diffraction and low transmissivity, leading to decreased image resolution, signal-to-noise ratio (SNR) degradation, starburst, and glare, which affect the quality of images acquired.
Innovation Solution
An electronic device with a processor that acquires sample frames, identifies light source objects, determines imaging parameters, composites multiple frames, and performs frame correction based on identified attributes to enhance image quality and mitigate optical diffraction effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a camera is disposed under a display having a shielding structure, then the display area is increased, but optical diffraction and low transmissivity occur leading to decreased image quality
Solution Approach 1:
The system performs preliminary identification of light source objects in sample frames before acquiring multiple frames. This preliminary action allows the system to detect potential optical diffraction issues early and adjust imaging parameters accordingly, preventing degradation before it occurs
Solution Approach 2:
The system uses feedback from light source object identification and attribute analysis to dynamically adjust imaging parameters and perform frame correction. The identification results feed back into the imaging process to optimize image quality while maintaining the UDC structure
2Manufacturing precision
If multiple frames are acquired and compositing is performed to improve image quality, then resolution and SNR are improved, but processing time and complexity increase
Solution Approach 1:
The system performs frame correction selectively based on light source object attributes rather than applying correction to all frames uniformly. This partial action approach reduces processing time while maintaining image quality by focusing computational resources only where needed
Solution Approach 2:
The system dynamically changes imaging parameters based on identified light source attributes. By adjusting parameters such as exposure time and gain according to the specific scene conditions, the system achieves high image quality without requiring excessive multiple frames, thus reducing processing time
3Object-affected harmful factors
If frame correction is performed based on light source attributes, then artifacts such as starburst and glare are removed, but computational complexity increases
Solution Approach 1:
The system extracts and identifies light source objects from the image data, separating the problematic light source regions from the rest of the image. This extraction allows targeted correction of optical artifacts like starburst and glare without requiring complex processing of the entire image
Solution Approach 2:
The system uses identified light source objects as intermediaries to guide the frame correction process. By using these identified objects as reference points, the correction algorithm can efficiently remove artifacts without requiring complex computational approaches across the entire 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
This approach improves image quality by preventing resolution reduction, SNR degradation, and removing artifacts such as starburst and glare, allowing for high-quality image acquisition across various environments.
Implementation Method 1
light incident from the outside is diffracted, thereby decreasing the transmissivity
Data Source
AI summary
An electronic device is provided. The electronic device includes a display, a camera module disposed under the display, and a processor electrically connected to the display and the camera module. The processor is configured to acquire a sample frame by using the camera module, identify whether a light source object is included in the sample frame, determine an imaging parameter for acquisition of first multiple frames when the light source object is identified to be included in the sample frame, acquire multiple frames, based on the imaging parameter, composite the multiple frames to generate a composite frame, identify an attribute of the light source object included in the composite frame, and perform frame correction of the composite frame, based on the identified attribute.