Convolutional Neural Network Filter Stacker for Colorization
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Solution Overview
Problem
Artificial neural networks (ANNs) trained on inputs with defects or artifacts suffer from reduced accuracy and performance due to the complexity of identifying and correcting these issues within the network, leading to potential overfitting and the need for extensive training data to cover all edge cases.
Innovation Solution
The implementation of image and document filtering modules that preprocess inputs to identify and correct specific defects or artifacts, with the outputs being processed by a de-noise ANN, which can be indirectly trained using a loss function based on ground truth images or documents, reducing the complexity of the de-noise ANN and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the de-noise ANN is trained directly on images with defects and artifacts, then it can potentially learn to correct all types of issues, but the complexity and dimensionality of the ANN increases significantly
Solution Approach 1:
The system segments the defect correction task into multiple specialized filtering modules, each targeting specific types of defects (e.g., denoising filter for noise, deblurring filter for blur, inpainting filter for missing regions). This segmentation allows the de-noise ANN to focus on a narrower task, reducing its complexity while maintaining comprehensive correction capabilities through the ensemble of specialized filters.
Solution Approach 2:
The system introduces intermediate filtering modules as mediators between the input image and the de-noise ANN. These filters preprocess the input by addressing specific defect types before the image reaches the ANN, thereby reducing the burden on the ANN to handle all defect types simultaneously and lowering its required dimensionality.
2Reliability
If the de-noise ANN is designed with high complexity to handle all defect types, then it may achieve comprehensive correction, but it requires extensive training data to cover all edge cases and suffers from overfitting
Solution Approach 1:
By segmenting the correction task into specialized filtering modules, each module can be trained on relatively small, targeted datasets specific to its defect type. This eliminates the need for one massive ANN to learn all defect types from extensive diverse training data, thereby reducing overall training data requirements while maintaining comprehensive correction capability.
Solution Approach 2:
The system employs self-service through automated filter selection and ensemble methods. The most appropriate filters are automatically selected and combined based on the input image characteristics, reducing the need for extensive manual tuning and large training datasets to cover all possible edge cases.
3Device complexity
If the de-noise ANN is simplified to reduce complexity, then training becomes easier, but the ability to adapt effectively to the task may be compromised
Solution Approach 1:
The system compensates for the simplified de-noise ANN by segmenting the overall task into multiple specialized filtering modules. Each module is optimized for specific defect types, and their combined output provides comprehensive adaptability even though the individual de-noise ANN has reduced complexity.
Solution Approach 2:
The system merges the outputs of multiple specialized filtering modules with the simplified de-noise ANN to achieve comprehensive defect correction. This combination allows the system to maintain high adaptability across different defect types while using a simpler, more manageable ANN architecture.
Data Source
AI summary
Methods and systems for training and utilizing an artificial neural network (ANN) are provided. In an example method, a computing device can receive an image pair, where a first image of the image pair includes a training image and a second image of the image pair includes a ground truth image. The computing device can provide instances of the first image to a plurality of image filtering modules and determine respective filtered representations of the first image using the plurality of image filtering modules. The computing device can indirectly train an adaptor ANN by applying the adaptor ANN on the respective filtered representations to produce an adapted representation; determining, using a trained colorization ANN, a colorized image from the adapted representation; and updating weights of the adaptor ANN based on a loss function that comprises a difference between the colorized image and the second image of the image pair.