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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Connecting Image Denoising and High-Level Vision Tasks via Deep Learning.

Ding Liu, Bihan Wen, Jianbo Jiao

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 17, 2020

Summary

This study jointly addresses image denoising and high-level vision tasks, showing that semantic information improves denoising and that denoising enhances vision task performance. Deep learning effectively fuses these tasks for better results.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computer Vision
Deep Learning
Image Processing

Background:

Conventional computer vision separates image denoising and high-level vision tasks, leading to a fragile connection.
The mutual influence and joint optimization of these tasks remain underexplored.

Purpose of the Study:

To investigate how image denoising can improve high-level vision tasks.
To explore how semantic information from high-level vision tasks can guide image denoising.
To develop a unified deep learning framework for joint denoising and high-level vision tasks.

Main Methods:

A convolutional neural network (CNN) for image denoising utilizing multi-scale contextual information through downsampling and upsampling.
A deep neural network (DNN) solution cascading separate denoising and high-level task modules.
Joint loss optimization for backpropagation, updating only the denoising network.

Main Results:

The proposed denoiser demonstrates generality, improving performance across various high-level vision tasks.
Guidance from high-level vision information leads to more visually appealing denoised images.
Simultaneous exploitation of image semantics benefits both denoising and high-level vision tasks.

Conclusions:

Deep learning provides an effective approach to jointly optimize image denoising and high-level vision tasks.
Integrating semantic information enhances image denoising quality and task performance.
The proposed framework offers a unified solution for improving computer vision pipelines.