Image Super-Resolution Using Warped Transforms and Adaptive Thresholding
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Solution Overview
Problem
Existing image and video signal processing methods, such as linear and non-linear interpolation filters, struggle to enhance the resolution of images and videos with complex regions like slant edges and textures, and require numerous iterations for achieving high-quality results, making them inefficient for real-time applications.
Innovation Solution
The proposed method employs warped transforms and adaptive thresholding to enhance the resolution of media, aligning transforms with edges for sparse representation and using spatially adaptive thresholds to achieve high-quality results with minimal iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If linear or non-linear interpolation filters are applied to enhance resolution, then smooth regions can be processed, but complex regions like slant edges and textures cannot be handled effectively
Solution Approach 1:
The patent applies different transform types (wavelet transforms, contourlet transforms, curvelet transforms) to different regions of the image based on their local characteristics. Smooth regions use one transform while complex regions with edges and textures use other transforms, allowing each region to be processed optimally for its specific properties.
Solution Approach 2:
The patent dynamically selects and adapts transform parameters based on the local content of each image region. The transform type and parameters are adjusted according to the detected characteristics (smoothness, edge orientation, texture complexity), enabling the system to adapt to varying image content rather than using a fixed transform approach.
2Manufacturing precision
If transform-based methods are used to increase resolution, then resolution enhancement can be achieved, but a large number of iterations are required
Solution Approach 1:
The patent performs preliminary actions by pre-computing transform coefficients and preparing transform databases before the main processing. This allows the actual resolution enhancement to proceed more quickly using pre-prepared materials, reducing the number of iterations needed during the main processing phase.
Solution Approach 2:
The patent changes transform parameters adaptively based on image content characteristics. By adjusting parameters such as transform type, decomposition level, and orientation angles according to local image properties, the system achieves high resolution with fewer iterations compared to fixed parameter approaches.
3Manufacturing precision
If existing transform-based methods are applied, then resolution can be increased, but the complexity becomes prohibitively expensive
Solution Approach 1:
The patent segments the image into different regions and applies appropriate transforms to each segment. This segmentation allows the system to use computationally efficient transforms for simple regions while reserving more complex transforms only for regions that actually require them, reducing overall computational complexity.
Solution Approach 2:
The patent applies different levels of computational complexity to different regions based on their content. Simple smooth regions use lightweight transforms while complex regions with edges and textures use more computationally intensive transforms, optimizing the balance between resolution quality and computational cost.
Data Source
AI summary
A method and apparatus is disclosed herein for a quality enhancement/super resolution technique. In one embodiment, the method comprises receiving a first version of media at a first resolution and creating a second version of the media at a second resolution higher or equal to the first resolution using at least one transform and adaptive thresholding.