Image Warping With B-Spline High-Frequency Feature Reconstruction
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Solution Overview
Problem
Existing image warping methods suffer from high computational costs and artifacts such as blur and moire due to the transformation of discrete information into continuous information using Fourier transforms, leading to overshoot or undershoot during geometric transformations.
Innovation Solution
An image processing method that involves obtaining high-frequency features from an input image, transforming them into B-spline representations using convolutional neural networks, and generating warped features through bilinear warping and multilayer perceptron networks to reconstruct images with reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Fourier transform is used to transform discrete information into continuous information for image warping, then image geometric transformation can be achieved, but computational cost increases and artifacts such as overshoot or undershoot occur
Solution Approach 1:
The patent segments the image processing into distinct frequency components using discrete cosine transform, separating high-frequency features from low-frequency components. This segmentation allows selective processing of different frequency bands, reducing the computational burden compared to applying Fourier transform to the entire image while maintaining warping accuracy.
Solution Approach 2:
The patent changes the transformation parameter from Fourier transform to discrete cosine transform, and further transforms frequency domain coefficients into spatial domain representations using inverse discrete cosine transform. This parameter change enables efficient computation while avoiding the artifacts associated with Fourier transform-based warping.
2Manufacturing precision
If Fourier transform is used to transform discrete information into continuous information for image warping, then image geometric transformation can be achieved, but artifacts such as blur and moire occur in warped areas
Solution Approach 1:
The patent applies preliminary action by extracting high-frequency features through discrete cosine transform before the warping operation, and then transforms these features into spatial domain representations. This preliminary processing prepares the image data in a form that is more resistant to warping-induced artifacts, preventing blur and moire effects rather than correcting them afterward.
Solution Approach 2:
The patent substitutes the Fourier transform mechanism with a discrete cosine transform followed by inverse discrete cosine transform mechanism. This substitution replaces the mathematical transformation that causes artifacts with an alternative transformation that preserves image quality during geometric warping operations.
3Adaptability or versatility
If discrete information is transformed into continuous information for image warping, then geometric transformation is achieved, but transformation accuracy decreases due to overshoot or undershoot
Solution Approach 1:
The patent performs dimensionality change by transforming frequency domain coefficients into spatial domain representations through inverse discrete cosine transform. This dimensional transformation allows the warping operation to be performed in a domain where accuracy is preserved, avoiding the overshoot and undershoot problems that occur when directly transforming discrete to continuous information.
Data Source
AI summary
Provided is a method of processing an image including obtaining an image feature from an input image, obtaining a first warped feature from the image feature, obtaining, by using coordinate information, a second warped feature from the input image, and generating a warped image by using the first warped feature and the second warped feature. The obtaining the first warped feature from the image feature includes obtaining, from the image feature, a high-frequency feature corresponding to a high-frequency region of the input image, transforming the high-frequency feature into a B-spline representation, and generating the first warped feature based on the image feature and the B-spline representation.