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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

A Local and Non-Local Features Based Feedback Network on Super-Resolution.

Yuhao Liu¹, Zhenzhong Chu², Bin Li³

¹College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel Local and Non-local features-based Feedback network (LNFSR) for Single Image Super-Resolution (SISR). LNFSR effectively combines different network blocks to enhance the extraction of diverse image features, outperforming existing state-of-the-art methods.

Keywords:

deep convolutional network dense skip block feedback network non-local self-attention single-image super-resolution

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Single Image Super-Resolution (SISR) has seen significant progress with Convolutional Neural Networks (CNNs) excelling at local features and self-attention networks at non-local features.
Realistic images contain diverse features, challenging single-block network performance in SISR.
Integrating complementary feature extraction mechanisms is crucial for advancing SISR.

Purpose of the Study:

To propose a novel feedback network for SISR that effectively leverages both local and non-local features.
To enhance the reconstruction performance of Single Image Super-Resolution by integrating diverse feature extraction blocks.
To address the limitations of single-block networks in handling complex image features.

Main Methods:

A new Local and Non-local features-based Feedback network for SR (LNFSR) is proposed.
LNFSR employs three cooperating blocks: a traditional deep convolutional network for local/non-local non-feedbackable information, a dense skip-based feedback block for local feedbackable information, and a non-local self-attention block for non-local feedbackable information.
Feature up-fusion-delivery blocks are introduced to facilitate feature propagation within the network iterations.

Main Results:

The proposed LNFSR effectively extracts different feature maps using specialized blocks.
Experimental results demonstrate that LNFSR outperforms existing state-of-the-art SISR algorithms.
The synergistic combination of local and non-local feature extraction significantly improves super-resolution performance.

Conclusions:

The LNFSR architecture successfully integrates diverse feature extraction strategies for improved SISR.
The method demonstrates superior performance compared to current state-of-the-art algorithms.
This work highlights the benefit of combining local and non-local feature processing in a feedback network for enhanced image super-resolution.