Activation Map Patch Selection for Accurate Image Noise Prediction
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Solution Overview
Problem
Existing noise detection methods in images, particularly those using convolutional neural networks, struggle with accurately predicting noise patterns in real-world images due to resizing effects that alter noise information and fail to identify localized noise distribution, especially when using artificial noise models.
Innovation Solution
A method utilizing activation maps from a convolutional neural network to identify target patches within images, trained on data from various devices, to predict noise by classifying these patches as noisy or non-noisy, and using a series of classifiers to estimate noise levels accurately without resizing, thereby enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image resizing is performed to meet processing speed requirements, then processing efficiency is improved, but noise information is lost or destroyed making prediction less accurate
Solution Approach 1:
The image is divided into multiple patches, and the system selectively processes only those patches that are likely to contain noise based on activation maps from a pre-trained classifier. This segmentation approach allows the system to maintain high processing speed by avoiding full-image processing while preserving noise information in relevant regions.
Solution Approach 2:
A first classifier is pre-trained on full-resolution images to generate activation maps that identify regions containing noise. These activation maps are then used to guide subsequent processing, allowing the system to prepare and identify noise-containing regions before actual noise prediction, thus avoiding the need to resize the entire image.
2Ease of manufacture
If artificial noise models are used for training, then training data availability is improved, but real-world noise detection accuracy deteriorates due to distribution differences
Solution Approach 1:
The system uses a multi-stage classification approach where different classifiers are trained for different purposes. The first classifier is trained on artificial noise models for robustness, while subsequent classifiers are trained on real-world noisy images to capture actual noise distributions. This parameter change in training data strategy allows the system to benefit from both artificial and real noise characteristics.
Solution Approach 2:
Activation maps serve as an intermediary that bridges the gap between artificial noise models and real-world noise detection. The activation maps generated from artificial noise training are used to identify regions in real images that are likely to contain noise, allowing the system to transfer knowledge from artificial to real noise scenarios effectively.
3Reliability
If full image processing is performed to capture all noise areas, then noise detection coverage is improved, but processing complexity and time increase
Solution Approach 1:
The image is segmented into multiple patches, and processing is applied selectively to patches identified as containing noise through activation maps. This reduces the overall processing complexity while maintaining coverage of noise-containing regions.
Solution Approach 2:
Instead of processing the entire image, the system performs partial processing only on regions identified as likely to contain noise based on activation maps. This partial action approach maintains noise detection coverage for relevant areas while significantly reducing processing complexity and computational resources required.
Data Source
AI summary
An image processing apparatus is provided which obtains and provides image data at a first scale as input to a first classifier trained based on images in the first scale to classify the image data in a first class or a second class, outputs, from the first classifier, activation map data and image array data, obtains at least target region of the image data at the first scale based on output of a second classifier that uses the activation map data and image array data from the first classifier, maps the at least one target region to image data at a second scale, extracts target region image data from each of the at least one target region of the image data at the second scale and classifies, as a first type of image or a second type of image.