Image Super Resolution via Patch Clustering and CNN Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image super resolution methods, particularly linear regression-based and CNN-based approaches, face limitations in effectively restoring high-frequency details and edge performance due to the complexity of low-level features and the need for massive filters, leading to blurry or overly-smoothed results.
Innovation Solution
The proposed method involves segmenting images into patch clusters based on feature maps, using pre-trained CNN models to process these clusters, and learning cluster-specific CNN models for image super resolution and enhancement, combining handcrafted feature extraction with CNN processing to improve feature extraction and error regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear regression-based or CNN-based methods are used for image super resolution, then the processing can be performed with relatively simple algorithms, but the restoration of high-frequency details and edge performance deteriorates resulting in blurry or overly-smoothed results
Solution Approach 1:
The image is segmented into multiple patches, and each patch is further divided into sub-regions based on feature maps. This segmentation allows different processing strategies to be applied to different regions, improving restoration precision while maintaining algorithmic efficiency through localized processing.
Solution Approach 2:
Different patches are processed using different pre-trained CNN models selected according to their feature map characteristics. This local quality approach ensures that each region receives processing optimized for its specific content, thereby improving high-frequency detail restoration and edge performance without requiring a single complex algorithm for the entire image.
2Adaptability or versatility
If massive filters are used to handle low-level features in image super resolution, then the processing capability is enhanced, but the device complexity and computational requirements increase
Solution Approach 1:
The image is divided into patches and sub-regions based on feature maps, allowing the system to process only relevant regions with appropriate models. This segmentation reduces the need for massive filters across the entire image while maintaining adaptability for different feature types in different regions.
Solution Approach 2:
The system changes parameters by selecting different pre-trained CNN models based on feature map characteristics of each patch. Instead of using massive filters in a single model, the system dynamically adjusts model selection to match the specific features of each region, reducing overall device complexity while maintaining versatility.
3Device complexity
If a single CNN model is used for all patches, then the device complexity is reduced, but the restoration precision for different patch types deteriorates
Solution Approach 1:
Different pre-trained CNN models are selected and applied to different patches based on their feature map characteristics. This local quality approach ensures that each patch receives processing optimized for its specific content type, improving restoration precision while maintaining manageable device complexity through model selection rather than model multiplication.
Solution Approach 2:
The system changes the model parameter by selecting from a set of pre-trained CNN models based on feature map analysis of each patch. This dynamic parameter change allows the system to adapt to different patch types without requiring a single overly complex model, thereby improving precision while controlling device complexity.
Data Source
AI summary
The disclosure provides methods and image processing devices for image super resolution, image enhancement, and convolutional neural network (CNN) model training. The method for image super resolution includes the following steps. An original image is received, and a feature map is extracted from the original image. The original image is segmented into original patches. Each of the original patches is classified respectively into one of patch clusters according to the feature map. The original patches are processed respectively by different pre-trained CNN models according to the belonging patch clusters to obtain predicted patches. A predicted image is generated based on the predicted patches.