Super-Resolved Image Processing via ROI Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques for generating high-quality images, such as those used in homing missiles, are often slow and computationally demanding, making them unsuitable for applications requiring rapid image processing and low hardware requirements.
Innovation Solution
A method for generating super-resolved images by iteratively calculating an updated super-resolved frame portion using a weighted combination of current and previous frame portions, where the contribution of each frame portion progressively decays with each successive calculation, allowing for real-time processing with reduced computational and memory demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional super-resolution techniques are used to generate high-quality images, then image quality is improved, but processing speed and computational efficiency deteriorate
Solution Approach 1:
The patent divides the image processing into regions of interest (ROI) and non-ROI areas. Full super-resolution is applied only to ROI portions where high quality is critical, while other areas receive simplified processing. This segmentation allows the system to maintain high image quality for important regions while significantly reducing overall computational demands and processing time.
Solution Approach 2:
The patent applies partial super-resolution processing only to necessary portions of the image (ROI) rather than processing the entire image at full resolution. By performing super-resolution selectively on specific regions containing targets or important features, the system achieves adequate image quality for the application's needs while reducing computational complexity and processing time compared to full-image super-resolution.
2Measurement precision
If high-resolution image processing is performed on entire frames, then image quality is improved, but memory requirements and device complexity increase
Solution Approach 1:
The patent segments the image frame into regions of interest and other areas, processing only the ROI portions at high resolution. This approach maintains image quality for critical regions while significantly reducing the amount of data that must be stored and processed in high resolution, thereby lowering memory requirements and device complexity.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. High-quality super-resolution is applied locally to regions containing targets or important features, while other regions receive lower-resolution processing. This local quality approach ensures adequate image quality where needed while reducing overall memory and computational requirements.
3Measurement precision
If multiple image frames are processed to achieve super-resolution, then measurement precision is improved, but processing time and computational load increase
Solution Approach 1:
The patent processes only regions of interest from multiple image frames rather than processing entire frames. By extracting and processing only the ROI portions containing targets or important features from each frame, the system maintains super-resolution accuracy for critical areas while significantly reducing processing time and computational load compared to processing complete frames.
Solution Approach 2:
The patent performs partial processing of image frames by focusing computational resources on ROI portions rather than processing the entire frame. This partial action approach achieves sufficient super-resolution accuracy for the application's needs while reducing processing time and computational requirements compared to processing all frame data at full resolution.
Data Source
Figure 1(a)~2
Figure 3~4
Figure 5
AI summary
An image-processing method includes obtaining an image including a target object, the image being formed by an array of pixels. A current frame portion is extracted from the image, the frame portion being at least a portion of the pixels forming the image, corresponding to a region of interest in the image, the region of interest comprising the target object. A previously calculated current super- resolved frame portion is provided, corresponding to the region of interest in the image. An updated super-resolved frame portion is calculated from the current frame portion and the current super-resolved frame portion.