Texture-Aware Blind Denoising with Self-Residual Learning
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Solution Overview
Problem
Conventional self-supervised blind denoising methods fail to effectively eliminate spatially correlated noise in real-world images without degrading texture details, as they rely on Pixel-shuffle Downsampling (PD) processes that destroy high-frequency details.
Innovation Solution
A blind denoising device and method that uses self-residual learning to eliminate noise without PD, employing a receiving unit, a Pixel-shuffle Downsampling (PD) unit, a predicted image producing unit, and a learning unit with self-supervised losses to restore high-frequency characteristics and eliminate spatially correlated noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Pixel-shuffle Downsampling (PD) is used to eliminate spatially correlated noise, then noise elimination performance is improved, but texture details are degraded
Solution Approach 1:
The image processing is segmented into two distinct phases: training phase where PD is applied to generate diverse downsampled images for robust noise pattern learning, and testing phase where PD is completely removed to preserve original texture details. This segmentation allows each phase to optimize for its specific goal without compromising the other.
Solution Approach 2:
The model is preliminarily trained using downsampled images with PD during the training phase, enabling the network to learn noise patterns and restoration capabilities in advance. This preliminary action equips the model with noise elimination skills that can be applied later without actually applying PD during the final denoising operation.
2Reliability
If PD-based inference is used in the testing phase, then noise elimination is enhanced, but high-frequency details are destroyed
Solution Approach 1:
The model serves itself by internally learning noise patterns during training without requiring external PD operations during testing. The network develops self-sufficient noise elimination capabilities through exposure to downsampled training data, enabling it to function effectively in the testing phase without actually applying PD, thus preserving high-frequency information.
3Manufacturing precision
If a model without PD process is used, then texture details are preserved, but noise elimination performance decreases
Solution Approach 1:
During training, the model processes copies of the original image that have been downsampled via PD. These copied and transformed versions help the network learn noise patterns without permanently altering the original image quality. The model learns from these copies while preserving the ability to restore the original image details in the testing phase.
Data Source
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AI summary
The present invention relates to a blind denoising device including: a receiving unit for receiving an original noisy image; a Pixel-shuffle Downsampling (PD) unit for performing PD for the original noisy image to produce at least one or more downsampled images; a predicted image producing unit for eliminating the noise from the original noisy image and the downsampled images to produce at least one or more predicted images restored to the shape of the original noisy image; and a learning unit for performing optimized learning for the predicted image producing unit, based on at least one or more self-supervised losses of the predicted images and the original noisy image.