Image Magnification via Wavelet Decomposition and Dual Interpolation
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Solution Overview
Problem
Current image amplification methods face challenges in maintaining image quality while minimizing computational resources, as they often require more complex algorithms for high-frequency components, which increase calculation load and can damage low-frequency components.
Innovation Solution
The method employs wavelet packet decomposition to separate high-frequency and low-frequency components, using different interpolation algorithms for each, and merging them through a wavelet packet inverse transform to ensure image quality while reducing operational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a complicated interpolation algorithm (e.g., cubic convolution) is used to amplify high-frequency components, then image quality is improved, but computational complexity and operation amount increase
Solution Approach 1:
The patent segments the image into high-frequency components and low-frequency components through wavelet packet decomposition. Different interpolation algorithms are applied to different segments: a simple algorithm for low-frequency components and a complicated algorithm for high-frequency components. This segmentation allows the system to achieve high image quality where needed while reducing overall computational complexity.
Solution Approach 2:
The patent applies different interpolation algorithms to different parts of the image based on their frequency characteristics. High-frequency components (edges and details) receive the more sophisticated cubic convolution interpolation, while low-frequency components use a simpler algorithm. This local differentiation optimizes image quality in critical regions without unnecessarily increasing computation throughout the entire image.
2Device complexity
If the same interpolation algorithm is used for both high-frequency and low-frequency components, then processing is simplified, but image quality cannot be optimized for each component type
Solution Approach 1:
The patent differentiates the processing quality for different frequency components. The low-frequency components use a simple interpolation algorithm (e.g., nearest neighbor), while high-frequency components use a complicated algorithm (e.g., cubic convolution). This local quality differentiation ensures that each component type receives the appropriate level of processing detail, optimizing overall image quality while maintaining reasonable processing complexity.
3Productivity
If a simple interpolation algorithm is used, then computational operation amount is reduced, but image quality deteriorates due to discontinuous pixel values and blocking effects
Solution Approach 1:
The patent segments the image processing task into two parts: low-frequency components processed with a simple algorithm and high-frequency components processed with a complicated algorithm. This segmentation allows the system to maintain computational efficiency for the majority of the image (low-frequency areas) while achieving high image quality where it matters most (high-frequency edges and details).
Solution Approach 2:
The patent applies different quality levels to different regions of the image. Simple interpolation is used for low-frequency components where computational efficiency is prioritized, while complicated interpolation is used for high-frequency components where image quality is prioritized. This local quality differentiation resolves the contradiction between computational efficiency and image quality by applying the right level of processing to the right regions.
Data Source
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AI summary
Embodiments of the present disclosure provide an image amplifying method, an image amplifying device, and a display apparatus, and relate to field of image processing technique, the method comprises: obtaining, by an image amplifying device, high-frequency components and low-frequency components of a source image; performing, by the image amplifying device, pixel interpolation on the low-frequency components of the source image through a first interpolation algorithm, to obtain a low-frequency sub-image; performing, by the image amplifying device, pixel interpolation on the high-frequency components of the source image through a second interpolation algorithm, to obtain a high-frequency sub-image; and merging, by the image amplifying device, the low-frequency sub-image and the high-frequency sub-image, to obtain a merged image; wherein the first interpolation algorithm and the second interpolation algorithm adopt different algorithms, so that it can ensure image quality of the amplified image while reducing the operation amount. Embodiments of the present disclosure are applied to image amplification.