Convolutional Auto-Encoder for Multi-Type Image Denoising
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Solution Overview
Problem
Existing image denoising technologies require multiple training iterations for each type of image distortion, making them inefficient for handling diverse and random types of image noise and blur.
Innovation Solution
A noise reduction convolutional auto-encoding device and method that uses a single model architecture with multi-stride encoding and decoding convolutional layers, skip-connections, and a combined loss function to adapt to different types of image distortions, allowing for efficient denoising and deblurring of various distortion types with a single training iteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image denoising methods are used for each distortion type separately, then specialized performance can be achieved, but training efficiency and adaptability deteriorate due to requiring multiple training iterations for each distortion type
Solution Approach 1:
The patent applies universality by designing a single convolutional auto-encoder model that can handle multiple types of image distortions (Gaussian noise, speckle noise, salt and pepper noise, Gaussian blur, motion blur) simultaneously. The model uses a unified architecture with multi-stride encoding and decoding convolutional layers that can adapt to different distortion types without requiring separate specialized models or multiple training iterations for each distortion type.
2Adaptability or versatility
If a single model architecture is used for all distortion types, then adaptability improves, but manufacturing precision and specialized performance may deteriorate
Solution Approach 1:
The patent applies parameter changes by utilizing multi-stride convolutional layers with different stride values (e.g., stride=2, stride=4) in the encoding and decoding stages. These parameter variations enable the single model to capture different levels of abstraction and detail for various distortion types while maintaining a unified architecture. The skip-connections also dynamically adjust feature maps based on the input distortion type, preserving specialized performance without requiring multiple models.
3Reliability
If deep network architectures like RED-Net are used, then denoising performance improves, but computational complexity and processing time worsen due to requiring very deep networks and lots of calculations
Solution Approach 1:
The patent applies segmentation by dividing the convolutional auto-encoder into distinct functional modules: multi-stride encoding convolutional layers for downsampling and feature extraction, skip-connection layers for feature transfer, and multi-stride decoding convolutional layers for upsampling and reconstruction. This modular segmentation allows the network to achieve effective denoising with a balanced depth, avoiding the excessive complexity of very deep networks like RED-Net while maintaining strong denoising performance across multiple distortion types.
Data Source
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AI summary
The noise reduction convolutional auto-encoding method includes the following tasks. A distorted image is received and input into a noise reduction convolutional auto-encoding model. In the noise reduction convolutional auto-encoding model, an image feature of the distorted image is transferred to a first deconvolution layer through skip-connection. A plurality of multi-stride encoding convolutional layers are performed for the distortion image to reduce a dimension. A same-dimensional encoding convolutional layer is then performed. According to the corresponding multi-stride encoding convolutional layers and same-dimensional encoding convolutional layers, the corresponding a plurality of decoding multi-stride convolutional layers and a same-dimensional decoding convolutional layers are upgraded. The result of up-scaled dimension is input into the same-dimensional decoding convolutional layer of a balanced channel using the first deconvolution layer. A reconstructed image is output from the same-dimensional decoding convolutional layer of the balanced channel.