Image Resolution Enhancement via Vector Projection
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Solution Overview
Problem
Conventional methods for increasing image resolution, such as interpolation and super-resolution, face challenges in achieving high processing speeds, particularly when dealing with moving pictures, leading to dropped frames due to high calculation costs.
Innovation Solution
The method involves calculating block sizes and positions in low- and high-resolution images, projecting vectors onto a linear manifold in a Euclidean space to generate increased resolution blocks, reducing the number of calculations required and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional super-resolution method satisfying reconstruction constraint is used, then image quality (sharpness) is improved, but calculation cost increases leading to lower processing speed
Solution Approach 1:
The invention divides the image into multiple blocks and processes each block independently. By segmenting the image into manageable units, the calculation complexity is reduced from processing the entire image at once to processing smaller blocks, thereby lowering overall calculation cost while maintaining image quality through systematic block-wise super-resolution processing
Solution Approach 2:
The invention applies different processing strategies to different blocks based on their local characteristics. By setting blocks at specific positions and processing them with appropriate super-resolution techniques, the method optimizes calculation resources locally while achieving global image quality improvement without requiring exhaustive processing of every pixel
2Manufacturing precision
If high-resolution generation satisfying reconstruction constraint is used, then image sharpness is improved, but calculation time increases causing frame drops in moving pictures
Solution Approach 1:
The invention segments the image processing task into independent blocks that can be processed in parallel. This segmentation reduces the sequential calculation time required for full-image super-resolution, enabling faster processing that meets real-time playback requirements while maintaining sharpness through block-wise reconstruction constraint satisfaction
Solution Approach 2:
The invention performs preliminary setup by calculating block sizes and positions before actual super-resolution processing. By pre-determining the processing framework and block configuration, the method reduces setup time and enables more efficient execution of the super-resolution algorithm, thereby reducing overall calculation time while preserving image sharpness
Data Source
AI summary
An image resolution increasing method include setting a first block which is included in a low resolution image and is located at a first position, and a second block which is included in a high resolution image and is located at a second position, and setting, as an increasing resolution block of the first block, a third block expressed by a second vector obtained by projecting a first vector representing the second block to a linear manifold as a set of vectors that indicate fourth blocks of the second block size, the fourth blocks becoming the first block due to reduced resolution, in a Euclidean space having, as the number of dimensions, a product of the number of pixels arranged vertically in the second block size and the number of pixels arranged horizontally in the second block size.