Adaptive Image Processing Neural Network Segmentation
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Solution Overview
Problem
Current computer vision tasks using deep neural networks face limitations in processing high-resolution images due to memory constraints, as they require significant computing resources and often cannot handle higher resolutions, leading to inefficiencies and loss of context information.
Innovation Solution
The approach involves decomposing digital images into overlapping regions of different resolutions, where a lower resolution region covers the entire image and higher resolution regions focus on specific areas, with neural networks sharing information through prolongation and restriction operations to preserve context and improve processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is processed at high resolution, then fine details can be analyzed, but significant computing resources (memory, CPU, time) are required
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) that are processed separately at high resolution, while the rest of the image is processed at lower resolution. This segmentation allows the system to focus computational resources only on areas requiring fine detail analysis, thereby resolving the contradiction between measurement precision and productivity.
Solution Approach 2:
Different resolution qualities are applied to different regions of the image based on their importance. High resolution is allocated to regions containing fine details or critical information, while lower resolution is used for less critical areas. This local quality approach maintains fine detail analysis capability while improving overall computing resource efficiency.
2Measurement precision
If multiple resolutions of the entire image are generated, then small features can be analyzed, but each resolution requires significant computing resources
Solution Approach 1:
Instead of generating multiple resolutions of the entire image, the system segments the image into ROIs and generates multiple resolutions only for these specific regions. This reduces the total quantity of image data that requires processing, thereby decreasing computing resource consumption while maintaining the ability to detect small features within the ROIs.
Solution Approach 2:
The system applies partial action by processing only the necessary portions (ROIs) at high resolution rather than the entire image. This partial processing approach reduces computing resource consumption while still achieving sufficient small feature detection capability for the critical regions.
3Measurement precision
If the input image resolution is increased, then fine details are preserved, but memory requirements of the neural network increase
Solution Approach 1:
The image is segmented into ROIs that are processed separately. Only these segmented regions are maintained at high resolution in memory, while the rest of the image uses lower resolution representations. This segmentation strategy preserves fine details in critical areas while significantly reducing overall memory requirements.
Solution Approach 2:
High resolution quality is applied locally only to regions where fine detail preservation is necessary, rather than uniformly across the entire image. This local quality approach maintains measurement precision for important features while reducing the total memory requirements by using lower resolution for less critical areas.
Data Source
AI summary
Technologies are described for performing adaptive high-resolution digital image processing using neural networks. For example, a number of different regions can be defined representing portions of a digital image. One of the regions covers the entire digital image at a reduced resolution. The other regions cover less than the entire digital image at resolutions higher than the region covering the entire digital image. Neural networks are then used to process each of the regions. The neural networks share information using prolongation and restriction operations. Prolongation operations propagate activations from a neural network operating on a lower resolution region to context zones of a neural network operating on a higher resolution region. Restriction operations propagate activations from the neural network operating on the higher resolution region back to the neural network operating on the lower resolution region.