Image Super-Resolution Training With Balanced Texture Sampling
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Solution Overview
Problem
Existing super-resolution methods based on deep neural networks suffer from a significant imbalance in the proportion of sampled samples, particularly due to the prevalence of smooth regions, leading to networks favoring smooth regions over textured areas, which results in a loss of clear details in the reconstruction process.
Innovation Solution
The method employs a pyramidal neural network structure that iteratively trains a target neural network model using a balanced sample set obtained by filtering original images based on intervals of sample information content, such as gradient or variance, ensuring equal sample distribution across intervals, and utilizes multi-norm regularization during training to enhance convergence efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random sampling is used to obtain training samples, then the sampling process is simple and fast, but the proportion of smooth regions and textured regions becomes imbalanced, causing the network to favor smooth regions
Solution Approach 1:
The patent segments the training sample set into multiple intervals based on sample information content (such as gradient magnitude or variance). Samples are divided into intervals according to their characteristics, ensuring that both smooth regions and textured regions are represented proportionally in the training data, thereby resolving the imbalance caused by random sampling
Solution Approach 2:
The patent changes the sampling parameter from uniform random sampling to interval-based stratified sampling. By using sample information content (gradient, variance) as the basis for interval division, the sampling method adapts to the actual distribution of image regions, ensuring balanced representation of different texture types while maintaining efficient sampling
2Ease of manufacture
If the network is trained with imbalanced samples, then the training process is straightforward, but the network loses detail information in textured regions during reconstruction
Solution Approach 1:
The patent segments the sample space into multiple intervals based on sample information content, ensuring that textured regions and smooth regions are distributed evenly across different intervals. This segmentation allows the network to receive balanced training data that preserves detail information in textured regions while maintaining training simplicity
Solution Approach 2:
The patent introduces a feedback mechanism where the network parameters are updated based on the reconstructed image quality and the original sample characteristics. The training process uses the interval-based sample distribution to guide parameter updates, ensuring that the network learns to preserve details in textured regions while maintaining overall reconstruction accuracy
Data Source
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AI summary
Provided are an image super-resolution processing method and apparatus, a device, and a storage medium. The image super-resolution processing method includes acquiring an image to be processed and performing super-resolution processing on the to-be-processed image by a target neural network model. The target neural network model is obtained by iteratively training a pyramidal neural network model by using a target image sample set. The target image samples are obtained by filtering original image samples according to a plurality of intervals of a sample information content. The number of image samples in each interval of the sample information content is the same.