Rolling Shutter Distortion Correction via CNN Motion Prediction
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Solution Overview
Problem
Rolling shutter cameras introduce distortions due to their row-by-row exposure mechanism, especially when the camera is in motion, making it challenging to correct these distortions using traditional geometric methods, which are often degenerate and intractable, especially under pure translational motion.
Innovation Solution
A structure-and-motion-aware convolutional neural network (CNN) is employed to learn the underlying geometry from a single rolling shutter image, predicting camera motion and depth maps to correct distortions by synthesizing training data from global shutter counterparts and using a rectification module to produce geometrically consistent and visually pleasant images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional geometric methods are used to correct rolling shutter distortions, then the correction process becomes intractable and degenerate, but using CNN-based methods introduces computational complexity
Solution Approach 1:
The patent replaces traditional geometric correction methods with a CNN-based deep learning system. The CNN model learns to predict camera motion and depth directly from rolling shutter images, substituting the intractable geometric computation with a trained neural network that provides both accuracy and computational efficiency.
Solution Approach 2:
The patent employs preliminary action by pre-training the CNN model on synthesized rolling shutter images before deployment. The training phase prepares the network to handle various motion scenarios, so that during actual correction, the pre-trained model can quickly predict camera motion and depth without requiring complex runtime computations.
2Ease of manufacture
If rolling shutter cameras are used to reduce cost, then manufacturing cost decreases, but image quality deteriorates due to motion-induced distortions
Solution Approach 1:
The patent converts the harmful rolling shutter distortion into a beneficial signal by training the CNN to recognize and interpret the distortion patterns. Instead of treating the row-by-row exposure artifact as merely harmful, the system learns to extract useful camera motion and depth information from the distortion itself, then uses this information to correct the image.
Solution Approach 2:
The patent replaces the need for expensive global shutter cameras with affordable rolling shutter cameras by introducing a computational correction layer. The CNN-based correction system compensates for the hardware limitation, allowing low-cost cameras to produce corrected images of comparable quality to expensive global shutter alternatives.
3Adaptability or versatility
If CNN-based correction is applied to enable SLAM/SFM applications, then application versatility improves, but processing time increases
Solution Approach 1:
The patent replaces complex geometric computation pipelines required for SLAM and SFM with a streamlined CNN-based approach. The network directly predicts camera motion and depth from rolling shutter images, providing the essential inputs for SLAM/SFM algorithms more efficiently than traditional geometric methods, thus enabling these applications with reduced processing overhead.
Data Source
AI summary
A method for correcting rolling shutter (RS) effects is presented. The method includes generating a plurality of images from a camera, synthesizing RS images from global shutter (GS) counterparts to generate training data to train the structure-and-motion-aware convolutional neural network (CNN), and predicting an RS camera motion and an RS depth map from a single RS image by employing a structure-and-motion-aware CNN to remove RS distortions from the single RS image.