High Resolution Image Reconstruction via Motion-Compensated Frequency Remapping
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Solution Overview
Problem
Existing high-resolution image reconstruction methods fail to incorporate high spatial frequency data from neighboring frames, resulting in blurry low-resolution image estimates that cannot produce high-quality large size prints.
Innovation Solution
The method generates a high-resolution image by combining a low spatial frequency component from a low-resolution frame with a motion-compensated high spatial frequency component from closely related high-resolution frames, using spatial interpolation and remapping techniques to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial interpolation is used to generate low spatial frequency image estimate from low-resolution image frame, then the image can be reconstructed, but the resulting estimate appears blurry because it does not incorporate high spatial frequency information from neighboring frames
Solution Approach 1:
The patent combines low spatial frequency components from spatial interpolation with high spatial frequency components extracted from neighboring high-resolution frames through motion compensation. This merging of frequency components from different sources resolves the blurriness issue while maintaining spatial resolution improvement.
Solution Approach 2:
Motion compensation acts as an intermediary that transfers high spatial frequency information from neighboring frames to the current low-resolution frame. It bridges the gap between different temporal frames, enabling the incorporation of high-frequency details without direct temporal alignment.
2Measurement precision
If non-linear image interpolation is used to improve spatial resolution, then the spatial resolution improves somewhat, but the method requires training with a set of parameters (images)
Solution Approach 1:
The system uses the available high-resolution neighboring frames themselves as the source of high spatial frequency information, rather than requiring external training data. The motion compensation and frequency decomposition processes are self-contained, eliminating the need for separate training phases with parameter sets.
3Measurement precision
If high spatial frequency data from closely related high-resolution image frames is utilized, then image quality improves, but the processing complexity increases
Solution Approach 1:
The patent segments the image into low spatial frequency components and high spatial frequency components. This segmentation allows independent processing of each frequency band, simplifying the overall processing by handling different frequency components separately rather than processing the entire image uniformly.
Solution Approach 2:
The patent applies different processing strategies to different frequency components: spatial interpolation for low-frequency components and motion-compensated extraction for high-frequency components. This local quality approach optimizes processing for each component's specific characteristics, improving overall image quality while managing complexity through specialized handling.
Data Source
AI summary
A technique of reconstructing a high resolution image from at least one image sequence of temporally related high and low resolution image frames wherein each of said high resolution image frames includes a low spatial frequency component and a high spatial frequency component is described. The high-resolution image reconstruction technique uses spatial interpolation to generate a low spatial frequency component from a low-resolution image frame of the image sequence. The technique is adapted to generate a high spatial frequency component from at least one high resolution image frame of the image sequence which is closely related to the low resolution image frame, and to remap the high spatial frequency component to a motion-compensated high spatial frequency component estimate of the low resolution image frame. The motion-compensated high spatial frequency component estimate is added to the generated low spatial frequency component to form a reconstructed high-resolution image of the low-resolution image frame.