Cloud Image Enhancement Using Reference-Matched HD Blocks
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Solution Overview
Problem
Existing image processing methods on mobile devices result in uniform image styles due to fixed algorithms, limiting imaging quality and failing to adapt to diverse scenes and environments, akin to the difference between the Forbidden City in summer and winter.
Innovation Solution
A cloud-based image processing system that segments low-definition images into blocks, retrieves and enhances them using high-definition image libraries, and stitches them back together to achieve high-definition outputs, leveraging geographic and environmental data for personalized image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a terminal uses a fixed algorithm to process images, then the processing speed is fast, but the image quality and adaptability to different scenes are limited
Solution Approach 1:
The patent segments the image processing task into multiple components: a lightweight model runs on the terminal for basic processing, while a more complex comprehensive model runs on the cloud server. This segmentation allows the terminal to maintain fast processing speed while the cloud server provides advanced adaptability through the comprehensive model that considers multiple scene factors.
Solution Approach 2:
The patent introduces an image quality assessment model as an intermediary between the terminal and cloud server. This intermediary evaluates the captured image and determines whether cloud-based enhancement is needed, enabling adaptive processing that balances speed and quality based on scene requirements.
2Manufacturing precision
If a terminal processes images locally with limited capability, then the processing speed is fast, but the image quality is low and cannot match professional cameras
Solution Approach 1:
The patent divides the processing workflow into two stages: fast local processing on the terminal for immediate feedback, and enhanced cloud-based processing on the server for final high-quality output. This segmentation allows users to experience fast response while ultimately achieving professional-grade image quality through server-side enhancement.
Solution Approach 2:
The terminal performs preliminary image capture and basic processing before submitting to the cloud server. This preliminary action ensures that the terminal maintains responsiveness and provides immediate feedback to users, while the subsequent cloud processing enhances quality without requiring the entire process to occur on the server.
3Manufacturing precision
If a terminal uses advanced image processing algorithms, then the image quality improves, but the cost of components increases
Solution Approach 1:
The patent positions the cloud server as an intermediary that provides advanced processing capabilities externally. Instead of embedding expensive high-performance processors and algorithms in every terminal device, the system leverages centralized server resources to deliver professional-grade image quality enhancement, reducing per-device costs while maintaining quality.
Solution Approach 2:
The terminal captures images and replicates the functionality of professional camera processing by submitting images to the cloud server, which applies sophisticated algorithms. This copying approach allows consumer devices to achieve professional-quality output without requiring professional-grade hardware, as the processing power is replicated through cloud services rather than embedded in each device.
4Adaptability or versatility
If a terminal processes all images with the same algorithm, then the processing is simple, but the image style cannot adapt to different environments and scenes
Solution Approach 1:
The patent implements a dynamic processing system where the image quality assessment model continuously evaluates captured images and dynamically determines the appropriate processing path. Based on scene characteristics, lighting conditions, and image quality metrics, the system adaptively routes images to either local or cloud-based enhancement, enabling flexible adaptation to different environments without requiring complex pre-configuration.
Solution Approach 2:
The system changes processing parameters dynamically based on scene analysis. The image quality assessment model analyzes various parameters such as lighting conditions, scene type, and image defects, then adjusts the processing strategy accordingly. This parameter-based adaptation allows the system to optimize image style for different environments without requiring a completely different algorithm for each scenario.
Data Source
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AI summary
An image processing method is provided, and relates to the field of computer vision. The method includes: A cloud server segments, based on a zoom ratio of a high-definition image in a high-definition image library, a low-definition image of a small zoom ratio that is sent by a terminal, to obtain a plurality of image blocks. For each image block, the cloud server may perform retrieval and matching in the high-definition image library to obtain one or more high-definition reference images, and then perform image quality improvement by using an image enhancement network that is based on the high-definition reference images, to obtain a plurality of high-definition image blocks. Finally, the cloud server stitches the plurality of high-definition image blocks into a super-definition image with higher resolution and higher definition, and returns the super-definition image to the terminal. In this way, with use of a processing capability of the cloud server, a photographed image obtained when the terminal photographs an image or a video not only has a relatively large field of view, but also has rich details and texture and definition that can be achieved in a case of a large zoom ratio.