Interpretive Scaler for Super Resolution Image Processing
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Solution Overview
Problem
Current super resolution processes rely on one-dimensional scalers that do not account for local image information, leading to poor initial estimates of high resolution frames, resulting in artifacts like 'jaggies' and slow convergence, especially in scenes with complex motion.
Innovation Solution
The use of an interpretive scaler that generates high resolution frames by analyzing local image content, providing a more accurate initial estimate and reducing artifacts, thereby improving the super resolution process's convergence speed and output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a one-dimensional scaler is used to generate the initial high resolution estimate, then the device complexity is reduced, but the manufacturing precision of the high resolution frame deteriorates due to artifacts like jaggies
Solution Approach 1:
The patent transitions from one-dimensional scaling (independent horizontal and vertical interpolation) to two-dimensional adaptive scaling that considers local image content and gradients in both dimensions simultaneously. This dimensional enhancement allows the scaler to preserve edges and diagonal features more accurately, reducing artifacts like jaggies while maintaining reasonable computational complexity through localized processing.
Solution Approach 2:
The patent implements local quality by analyzing gradient directions and image content in local regions to adaptively determine scaling parameters. Different regions of the image receive different scaling treatments based on their local characteristics (e.g., edge regions vs. smooth regions), which improves overall precision without requiring a complete redesign of the entire scaling system.
2Ease of operation
If a non-adaptive scaler is used, then the ease of operation is improved, but the convergence speed of the super resolution process deteriorates due to poor initial estimates
Solution Approach 1:
The patent applies preliminary action by performing local gradient analysis and adaptive scaling before the main super resolution iteration process. This preliminary estimation of the high resolution frame provides a much better starting point for the iterative optimization, significantly reducing the number of iterations needed to converge while maintaining the relative simplicity of the overall workflow.
Solution Approach 2:
The patent changes scaling parameters dynamically based on local image characteristics such as gradient magnitude and direction. By adapting scaling parameters to local content rather than using fixed parameters throughout the image, the system achieves faster convergence without substantially increasing operational complexity for the end user.
3Adaptability or versatility
If motion estimation is used in scenes with complex motion, then the adaptability is improved, but the measurement precision of motion vectors deteriorates when meaningful matches cannot be found
Solution Approach 1:
The patent introduces an interpretive scaler as an intermediary between the low resolution input frames and the high resolution output. This intermediary uses local gradient information and adaptive scaling to generate a more accurate initial high resolution estimate that preserves structural information even when motion estimation fails, effectively mediating the information loss that would otherwise occur in complex motion scenarios.
Solution Approach 2:
The patent performs preliminary adaptive scaling and gradient-based enhancement before motion estimation and super resolution iterations. This preliminary processing establishes a more accurate structural framework that helps guide subsequent motion estimation and reduces the impact of failed motion matches, improving overall measurement precision in complex motion scenes.
Data Source
AI summary
A method of generating an initial high resolution frame includes receiving at least two low resolution frames of an image at a processor, wherein the low resolution frames have a resolution lower than the high resolution frame, using one or more low resolution frames to interpolate a high resolution frame using an interpretive scaler, wherein the interpolation adapts to the contours of the image, and using the initial high resolution frame and the low resolution frame in an iterative super resolution process.