Image Processing Model Generation via Receptive Field Expansion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques face challenges in efficiently processing high-resolution images due to increased computational resources and processing speed requirements, limiting their performance in environments with limited computing resources.
Innovation Solution
A method and apparatus for context-based adaptive image processing that increases the receptive field of input image frames, generates feature maps, and creates a replacement model using a processor, which includes modules for receptive field increase, feature extraction, and super resolution, allowing for efficient processing of high-resolution images by optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional super resolution techniques are used to process high-resolution images, then image quality can be improved, but computational resources and processing speed increase exponentially
Solution Approach 1:
The patent divides the image processing task into multiple stages: receptive field expansion module, feature extraction module, and super resolution module. Each module handles a specific aspect of the processing, allowing for more efficient computation compared to conventional monolithic approaches. The feature map is generated in stages rather than all at once, reducing the computational burden at any single point.
Solution Approach 2:
The patent transforms the input image into a feature map with expanded receptive field, effectively changing the dimensional representation of the data. By operating in this transformed feature space rather than directly on pixel values, the system can achieve high-resolution reconstruction with reduced computational complexity.
2Manufacturing precision
If conventional super resolution techniques are used to process high-resolution images, then image quality can be improved, but computational resources increase
Solution Approach 1:
The processing pipeline is segmented into distinct functional modules (receptive field expansion, feature extraction, super resolution), allowing computational resources to be allocated more efficiently across different processing stages rather than requiring peak resources for the entire operation simultaneously.
Solution Approach 2:
The feature map serves as an intermediary representation between the input low-resolution image and the output high-resolution image. By computing features in this intermediate space with expanded receptive field, the system avoids the exponential computational cost of directly processing high-resolution pixels.
3Productivity
If the receptive field is increased to improve image processing performance, then processing efficiency can be improved, but model complexity increases
Solution Approach 1:
The receptive field expansion is achieved through a dedicated module that operates separately from the main super resolution network. This segmentation allows the system to benefit from expanded receptive field without increasing the complexity of the core super resolution model, as the expansion is handled by a specialized preprocessing component.
Solution Approach 2:
The feature extraction module serves multiple functions: it processes the expanded receptive field information, extracts relevant features for super resolution, and prepares the data for the next processing stage. This multi-functionality reduces the need for separate specialized modules, thereby controlling overall model complexity.
Data Source
AI summary
A method of generating a model for image processing includes increasing, using a receptive field (RF) increasing module of at least one processor, a receptive field of an input image frame; generating, using a feature extraction (FE) module of the at least one processor, a feature map based on the input image frame with an increased receptive field; generating, using a super resolution (SR) module of the at least one processor, a target image having a target resolution, based on the feature map; and generating, using a model generation (MG) module of the at least one processor, a replacement model that replaces at least one of the RF increasing module, the FE module, and the SR module.