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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Denoising Prior Driven Deep Neural Network for Image Restoration.

Weisheng Dong, Peiyao Wang, Wotao Yin

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 9, 2018

Summary

This study introduces a novel deep neural network for image restoration that integrates observation models. The method achieves state-of-the-art results in denoising, super-resolution, and deblurring by combining deep learning with image degradation priors.

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Deep neural networks (DNNs) show promise for image restoration (IR).
Existing DNN methods often ignore observation models of image degradation.
Designing effective DNN architectures for IR remains challenging.

Purpose of the Study:

To propose a novel deep neural network architecture for image restoration that incorporates observation models.
To develop an efficient, denoising-based iterative algorithm for IR.
To improve image restoration performance by leveraging both DNN denoising capabilities and observation model priors.

Main Methods:

An iterative denoising-based algorithm for image restoration was developed.
The iterative algorithm was unfolded into a deep neural network architecture.
The network consists of denoiser modules and back-projection (BP) modules for observation consistency.
A convolutional neural network (CNN) based denoiser was designed to exploit multi-scale redundancies.
End-to-end training jointly optimized denoisers and BP modules.

Main Results:

The proposed method demonstrated competitive and state-of-the-art performance on various IR tasks.
Experimental results were validated on image denoising, super-resolution, and deblurring tasks.
The network effectively combined DNN denoising power with observation model priors.

Conclusions:

The proposed deep neural network architecture offers a significant advancement in image restoration.
Integrating observation models into DNNs enhances performance for tasks like denoising, super-resolution, and deblurring.
The method provides a robust framework for future research in image restoration.