Image Noise Removal via Feature Extraction and Neural Dropout
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Solution Overview
Problem
Current image processing technologies are inefficient and unreliable in detecting and removing noise artifacts from images, often mistakenly removing desired content or failing to remove noise altogether.
Innovation Solution
A system trained on clean and noisy images extracts features to identify noise artifacts, using a neural network dropout layer to filter out noise from image vectors, and adapts its processing based on the device's capability to ensure efficient noise removal across various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current image analysis and processing technologies are used to remove noise artifacts, then some noise removal may be achieved, but desired content (letters, numbers, etc.) is mistakenly removed and noise artifacts are not reliably removed
Solution Approach 1:
The patent segments the image processing task into multiple stages: training phase where the system learns to distinguish noise from content, and execution phase where extracted features are compared against training data. This segmentation allows the system to develop specialized detection capabilities that reduce false positives and preserve desired content while removing noise artifacts.
Solution Approach 2:
The patent implements preliminary action through the training phase that occurs before actual noise removal. The system is pre-trained with clean images and noisy images to learn the characteristics of noise artifacts versus desired content. This preliminary learning enables the system to make accurate distinctions during execution, preventing both noise removal failures and unwanted content deletion.
2Measurement precision
If image processing is performed to detect and remove noise artifacts, then image clarity may be improved, but processing time and computational resources increase
Solution Approach 1:
The training phase performs preliminary computation to establish feature extraction patterns and comparison criteria. By pre-processing and learning during the training phase, the system reduces the computational burden during actual noise removal operations, achieving high detection accuracy without excessive processing time for each image.
Solution Approach 2:
The system creates feature representations (copies) of images during training that capture essential characteristics without requiring full image processing during execution. These feature vectors serve as simplified copies that enable fast comparison and noise detection while maintaining high accuracy.
3Reliability
If comprehensive feature extraction and comparison is performed to accurately identify noise artifacts, then noise detection reliability improves, but device resource requirements (memory and processing power) increase
Solution Approach 1:
The patent extracts only the essential features needed for noise detection rather than processing entire images. By taking out and isolating specific feature elements that characterize noise artifacts, the system achieves high identification accuracy while significantly reducing memory and processing requirements compared to comprehensive image analysis.
Solution Approach 2:
The system transforms images into feature parameter representations during training and execution. This parameter transformation converts complex image data into simplified feature vectors that maintain discriminative power for noise detection while reducing computational complexity and memory requirements for processing.
Data Source
AI summary
A system for removing a noise artifact from an image of a document extracts a first set of features from the image, where the first set of features represents items on the image. The system identifies noise artifact features from the first set of features representing pixel values of the noise artifact. The system generates a second set of features by removing the noise artifact features from the first set of features. The system generates a test clean image of the document based on the second set of features as an input. The system determines whether a portion of the test clean image that previously displayed the noise artifact corresponds to a counterpart portion of the training clean image. If it is determined that the portion of the test clean image corresponds to the counterpart portion of the training clean image, the system outputs the test clean image.