Wide-Angle Image Calibration via Foreground Segmentation
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Solution Overview
Problem
Wide-angle camera images often exhibit significant distortion in corner regions, leading to unnatural appearances of objects like humans and buildings during correction, and existing methods fail to fully eliminate distortions in the background near the foreground.
Innovation Solution
An image calibration method that applies primary lens distortion correction, segments the foreground and background, performs secondary distortion correction using machine learning to align and morph feature pixels, and fills residual empty pixels by extending the background image, ensuring a non-deformed foreground and undistorted background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If distortion correction is applied to the corner region of the wide-angle image, then the distortion in the corner region is reduced, but objects in the region such as human or building will be looked unnaturally after correction
Solution Approach 1:
The image is segmented into foreground and background regions. Different correction strategies are applied to each region: the background undergoes geometric transformation to straighten lines, while the foreground objects are preserved with minimal distortion to maintain their natural appearance. This segmentation allows simultaneous optimization of both distortion correction and object naturalness.
Solution Approach 2:
Different quality requirements are applied to different regions of the image. The background region prioritizes geometric accuracy and straight lines, while the foreground region prioritizes preserving the natural appearance of objects. This local quality approach allows each region to be optimized according to its specific requirements without compromising the other.
2Manufacturing precision
If the background is calibrated to transform distorted lines into straight lines, then the background geometry is improved, but a part of the background close to the foreground still has unnatural distortion
Solution Approach 1:
The image is divided into foreground and background segments. The background segmentation allows independent processing of background regions, enabling geometric correction of distant background elements while preserving the appearance of foreground objects. This segmentation prevents the propagation of unnatural distortion from the background to the foreground region.
Solution Approach 2:
A mask or transition region acts as an intermediary between the foreground and background processing. This intermediary ensures that the geometric transformation applied to the background does not directly affect the foreground objects, allowing smooth transition and preventing unnatural distortion in the boundary region between foreground and background.
3Manufacturing precision
If foreground and background are segmented and different corrections are applied, then both regions can be optimized, but the processing complexity increases
Solution Approach 1:
The image is segmented into foreground and background using automated algorithms that identify object boundaries. This segmentation enables independent optimization of each region with appropriate correction methods, achieving high overall calibration quality. The automation of segmentation reduces the manual processing complexity despite the increased computational steps.
Solution Approach 2:
Different correction parameters and transformation models are applied to different regions based on their characteristics. The system dynamically adjusts processing parameters for foreground versus background regions, allowing optimal correction for each while managing complexity through parameterized processing pipelines rather than fixed complex procedures.
Data Source
AI summary
An image calibration method applied to a wide-angle image and executed by an image calibration apparatus includes applying primary lens distortion correction for the wide-angle image to generate a corrected image, segmenting an foreground image from the corrected image to generate a background image, applying secondary distortion correction for the foreground image based on the pre-defined object to generate a calibrated foreground image, fusing the background image with the calibrated foreground image to generate a fused image, detecting at least one residual empty pixel not overlapped by the calibrated foreground image within the fused image, and utilizing a machine learning algorithm to fill the at least one residual empty pixel of the fused image by extending the background image to provide an output image. The foreground image contains feature pixels relate to a pre-defined object and the background image has empty pixels corresponding to the foreground image.