Super-resolution Image Processing Using Feature Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current super-resolution image processing methods are computationally intensive and often result in blurring or lack of clarity, especially when increasing image resolution, as they do not effectively utilize non-intensity features like texture and depth to enhance image detail.
Innovation Solution
A method involving a machine learning system that processes both pixel intensity data and feature data, such as texture and depth, to generate a higher resolution image, using a neural network architecture that includes convolutional and deconvolutional layers to accurately increase image resolution while reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional super-resolution image processing methods are used to increase image resolution, then the resolution is improved, but the computational resources required increase significantly and the image quality deteriorates due to blurring
Solution Approach 1:
The system performs preliminary action by extracting and storing feature data (depth, texture, surface normals) from the input image before the super-resolution processing. This pre-extracted feature data is then reused during the upscaling process, avoiding the need for computationally intensive real-time calculations and reducing the overall computational burden while maintaining high resolution and image quality
2Measurement precision
If traditional super-resolution image processing methods are used to increase image resolution, then the resolution is improved, but the image quality deteriorates due to blurring and lack of clarity
Solution Approach 1:
The system introduces an intermediary mechanism by using extracted feature data (depth maps, texture information, surface normals) as mediators between the low-resolution input image and the high-resolution output. These feature maps serve as guiding intermediaries that preserve structural integrity and detail during the upscaling process, preventing blurring and maintaining image quality while achieving higher resolution
3Productivity
If feature data extraction and processing is added to the super-resolution system, then the system complexity increases, but the processing efficiency improves
Solution Approach 1:
The system applies segmentation by dividing the super-resolution process into distinct modular stages: feature extraction module, feature processing module, and image reconstruction module. Each module handles a specific aspect of the processing, making the overall complex system more manageable and efficient. The feature extraction can be performed separately and stored, allowing the main processing to focus on upscaling with pre-computed features
Data Source
AI summary
A method of super-resolution image processing. The method includes inputting first image data representative of a first version of at least part of an image with a first resolution to a machine learning system. The first image data includes pixel intensity data representative of an intensity value of at least one color channel of a pixel of the first version of the at least part of the image, and feature data representative of a value of at least one non-intensity feature associated with the pixel. The first image data is processed using the machine learning system to generate second image data representative of a second version of the at least part of the image with a second resolution greater than the first resolution.