Iterative Patch-Based Image Upscaling for Edge Preservation
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Solution Overview
Problem
Conventional image upscaling techniques often result in blurred image edges and textures, making the upscaled images undesirable due to the assumption of smoothness in up-sampled results.
Innovation Solution
The described techniques involve iterative upscaling with content-adaptive patch finding and metric techniques to minimize structure distortion, using Bicubic interpolation and Gaussian smoothing, and adaptive parameter updates to optimize resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If conventional upscaling techniques are used to increase image size, then the number of pixels increases, but image edges and textures become blurred
Solution Approach 1:
The patent changes the fundamental parameter of the upscaling approach by using iterative refinement with multiple passes at different scale factors (e.g., 1.5x, 1.33x, 1.25x) rather than a single large-scale transformation. This iterative parameter adjustment allows progressive enhancement of image details while maintaining edge and texture sharpness throughout the process.
Solution Approach 2:
The patent segments the upscaling process into multiple iterative stages, each handling a portion of the total scaling task. By dividing the overall upscaling into smaller incremental steps with intermediate processing stages, the system preserves local image structures and avoids the blurring that occurs in single-step conventional upscaling.
2Manufacturing precision
If iterative upscaling with multiple processing stages is used to preserve image details, then image quality improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by performing iterative upscaling only to the extent necessary to achieve the target resolution, using adaptive termination criteria. The process performs fewer iterations when the image reaches sufficient quality or when computational resources are constrained, avoiding unnecessary computational overhead while maintaining essential image detail preservation.
Solution Approach 2:
The patent introduces dynamic adaptation in the upscaling process by adjusting processing parameters, iteration counts, and scale factors based on image content characteristics and computational resource availability. This dynamic approach allows the system to optimize between quality and complexity in real-time, reducing computational burden when full iterative processing is not required.
3Ease of manufacture
If conventional upscaling assumes smoothness in up-sampled results, then computation is simplified, but image edges and textures are blurred
Solution Approach 1:
The patent applies preliminary anti-action by explicitly preventing the blurring effect that conventional smoothness assumptions cause. Through iterative refinement stages and content-aware processing, the system counteracts the natural tendency toward smoothing, preserving edges and textures by applying targeted enhancements that oppose the blurring effect inherent in conventional upscaling methods.
Data Source
AI summary
Image upscaling techniques are described. These techniques may include use of iterative and adjustment upscaling techniques to upscale an input image. A variety of functionality may be incorporated as part of these techniques, examples of which include content-adaptive patch finding techniques that may be employed to give preference to an in-place patch to minimize structure distortion. In another example, content metric techniques may be employed to assign weights for combining patches. In a further example, algorithm parameters may be adapted with respect to algorithm iterations, which may be performed to increase efficiency of computing device resource utilization and speed of performance. For instance, algorithm parameters may be adapted to enforce a minimum and/or maximum number to iterations, cease iterations for image sizes over a threshold amount, set sampling step sizes for patches, employ techniques based on color channels (which may include independence and joint processing techniques), and so on.