18247562. NOISE LEARNING-BASED DENOISING AUTOENCODER simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

NOISE LEARNING-BASED DENOISING AUTOENCODER

Organization Name

Telefonaktiebolaget LM Ericsson (publ)

Inventor(s)

Woonghee Lee of Seoul (KR)

Ursula Challita of Solna (SE)

Jingya Li of Göteborg (SE)

NOISE LEARNING-BASED DENOISING AUTOENCODER - A simplified explanation of the abstract

This abstract first appeared for US patent application 18247562 titled 'NOISE LEARNING-BASED DENOISING AUTOENCODER

Simplified Explanation

Methods and apparatuses for noise learning-based denoising of noisy input data Y that is equal to the original data X plus the noise N (i.e., Y=X+N). In contrast with a conventional denoising autoencoder (DAE) method that attempts to learn the original data X directly from noisy input data Y, the noise learning-based denoising learns the noise N in the noisy input data Y and then regenerates the original data X by subtracting the learned noise N from the noisy input data Y. Learning the noise N may include inputting the noisy input data Y into an encoder of a neural network, and the learned noise N may be output from a decoder of the neural network. Training the neural network may include inputting noisy training data into an encoder of the neural network and outputting training noise from a decoder of the neural network.

• Noise learning-based denoising method for noisy input data Y (Y=X+N)
• Learns the noise N in the noisy input data Y to regenerate the original data X
• Utilizes a neural network with an encoder and decoder for noise learning
• Training involves inputting noisy training data to the encoder and outputting training noise from the decoder

Potential Applications

• Image denoising
• Speech enhancement
• Signal processing

Problems Solved

• Removing noise from input data
• Improving data quality
• Enhancing the accuracy of data analysis

Benefits

• Higher quality output data
• Improved performance of machine learning models
• Enhanced signal-to-noise ratio

Possible Prior Art

There are existing denoising methods such as DAEs and traditional filtering techniques that aim to remove noise from input data.

How does the noise learning-based denoising method compare to traditional denoising techniques in terms of performance and accuracy?

The article does not provide a direct comparison between the noise learning-based denoising method and traditional denoising techniques. Further research or experimentation may be needed to evaluate the performance and accuracy differences between these methods.

What are the computational requirements for implementing the noise learning-based denoising method in real-world applications?

The article does not discuss the computational resources or requirements needed to implement the noise learning-based denoising method. Understanding the computational aspects of this method is crucial for practical deployment in various applications.

Original Abstract Submitted

Methods and apparatuses for noise learning-based denoising of noisy input data Y that is equal to the original data X plus the noise N (i.e., Y=X+N). In contrast with a conventional denoising autoencoder (DAE) method that attempts to learn the original data X directly from noisy input data Y, the noise learning-based denoising learns the noise N in the noisy input data Y and then regenerates the original data X by subtracting the learned noise N from the noisy input data Y. Learning the noise N may include inputting the noisy input data Y into an encoder of a neural network, and the learned noise N may be output from a decoder of the neural network. Training the neural network may include inputting noisy training data into an encoder of the neural network and outputting training noise from a decoder of the neural network.