Super-Resolution Image Processing via Block Matching
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Solution Overview
Problem
Current super-resolution technologies face high calculation complexity and memory resource occupancy due to processing initial high-resolution images, which are derived from low-resolution images, leading to inefficient image processing.
Innovation Solution
The method involves performing image magnification on a low-resolution image to obtain an initial high-resolution image, selecting a central similar block from the initial high-resolution image, and a search block from the low-resolution image of the same size, calculating similarity using Euclidean distance, and applying weighted averaging to determine pixel values, thereby reducing calculation complexity and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing is performed on an initial high-resolution image obtained from low-resolution image, then image quality is improved, but calculation complexity increases
Solution Approach 1:
The patent divides the image into multiple blocks and processes each block separately using block-matching algorithms. This segmentation approach reduces the overall calculation complexity by breaking down the large-scale image processing into smaller, manageable units that can be handled more efficiently.
Solution Approach 2:
The patent performs preliminary actions by first identifying similar blocks and calculating their similarity metrics before performing the actual image processing. This preliminary block-matching and similarity calculation prepares the data in advance, reducing the computational burden during the main processing stage.
2Manufacturing precision
If image processing is performed on an initial high-resolution image, then image quality is improved, but memory resource occupancy increases
Solution Approach 1:
By dividing the high-resolution image into smaller blocks, the patent reduces the memory required to hold and process the entire image at once. Each block can be processed independently with minimal memory overhead, significantly reducing total memory resource occupancy.
Solution Approach 2:
The patent extracts only the necessary block information (such as similarity metrics and displacement vectors) from the low-resolution image rather than processing the entire high-resolution image data. This extraction approach minimizes memory usage by keeping only the essential processing data in memory.
3Productivity
If block size is increased to improve processing efficiency, then processing speed is improved, but processing precision decreases
Solution Approach 1:
The patent dynamically adjusts the block size based on the specific processing requirements and image characteristics. By making the block size adaptable rather than fixed, the system can optimize for both processing efficiency and precision in different scenarios, resolving the trade-off between speed and accuracy.
Solution Approach 2:
The patent applies different processing approaches to different regions of the image by using local block-matching. Each block can be processed with appropriate parameters tailored to its specific characteristics, maintaining high precision locally while achieving overall processing efficiency through the distributed block-based approach.
Data Source
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AI summary
Embodiments of the present invention provide an image processing method and apparatus, where the image processing method includes: performing image magnification processing on a low-resolution image to obtain an initial high-resolution image; selecting a central similar block corresponding to a first pixel from the initial high-resolution image; selecting a search block corresponding to the first pixel from the low-resolution image, and determining each similar block in the search block, where the central similar block and the search block have a same block size; and determining a pixel value of the first pixel according to the central similar block and each similar block in the search block. By using technical solutions in the embodiments of the present invention, calculation complexity and an occupied memory resource can be effectively reduced.