Neural Image Enhancement Training With Paired Ground Truth Data
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Solution Overview
Problem
Existing image enhancement technologies face challenges in achieving high-performance real-time image enhancement due to limitations in traditional filtering methods and unsupervised learning, which often result in suboptimal results without actual ground truth data.
Innovation Solution
A method involving supervised learning using a neural network model trained with pairs of input and target data, generated from sample images enhanced by non-real-time image enhancement software, to output enhanced images in response to low-quality inputs, specifically tailored for different camera classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional filtering methods are used for image enhancement, then real-time processing is achieved, but image enhancement quality is suboptimal
Solution Approach 1:
The patent replaces traditional mechanical filtering methods with a neural network-based machine learning system. The neural network model learns optimal enhancement parameters from training data consisting of paired low-quality and high-quality images, enabling real-time enhancement with superior quality by substituting the mechanical filtering approach with an intelligent adaptive system.
Solution Approach 2:
The patent performs preliminary training of the neural network model using extensive training data before actual image enhancement. The training phase pre-computes optimal enhancement strategies by learning from paired images, so that during real-time operation, the model can directly apply learned patterns without computationally intensive processing, achieving both speed and quality.
2Ease of manufacture
If unsupervised learning is used for image enhancement, then training without ground truth data is possible, but enhancement accuracy deteriorates
Solution Approach 1:
The patent implements supervised learning where the neural network receives feedback in the form of ground truth high-quality images during training. The model learns to map low-quality inputs to high-quality outputs by minimizing the difference between its predictions and the actual ground truth images, providing accurate feedback signals that guide the learning process and improve enhancement accuracy.
Solution Approach 2:
The patent performs preliminary preparation of training data by collecting and pairing low-quality images with their corresponding high-quality ground truth versions before training begins. This pre-prepared training dataset with known correct answers enables the supervised learning process to proceed efficiently with accurate target values for each training example.
3Device complexity
If a single neural network model is trained for all camera types, then device complexity is reduced, but enhancement performance for specific camera classes deteriorates
Solution Approach 1:
The patent segments the training process and model deployment by camera class or type. Different neural network models are trained separately for different camera classes using camera-specific training data, allowing each model to learn the particular characteristics and noise patterns of its target camera type. This segmentation enables specialized optimization for each camera class while maintaining overall system manageability.
Solution Approach 2:
The patent applies local quality optimization by training separate models with camera-specific parameters and characteristics. Each neural network model is customized for its specific camera class, learning the unique degradation patterns, noise characteristics, and optical properties of that camera type, thereby achieving locally optimized enhancement quality for each camera segment.
Data Source
AI summary
A method and an apparatus for training an image-enhanced neural network model are provided. According to one embodiment, the training method may comprise the steps of: acquiring sample images having various image qualities; generating enhanced images of at least some of the sample images by using image enhancement software having an image enhancement function; constructing, from the sample images and the enhanced images, training data that forms pairs of input data and target data; using the training data so as to output an enhanced output image in response to input of a low-image-quality input image; and performing supervised training on a first neural network model for outputting a corresponding-image-quality output image in response to input of a high-image-quality input image.