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Related Experiment Video

Updated: Aug 27, 2025

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

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Published on: December 15, 2023

Multi-Scale Attention-Guided Non-Local Network for HDR Image Reconstruction.

Howoon Yoon¹, S M Nadim Uddin¹, Yong Ju Jung¹

¹School of Computing, Gachon University, Seongnam 13120, Korea.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces MSANLnet, a novel network for high-dynamic-range (HDR) image reconstruction. It effectively reduces ghosting artifacts by using multi-scale attention and non-local fusion for improved HDR image quality.

Keywords:

deep learning high-dynamic-range imaging non-local means spatial attention

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Current Convolutional Neural Network (CNN)-based methods for high-dynamic-range (HDR) image reconstruction often struggle with ghosting artifacts.
These artifacts arise from object or camera movement during the capture of multiple low-dynamic-range (LDR) images.

Purpose of the Study:

To propose an efficient HDR image reconstruction method that mitigates ghosting artifacts.
To enhance the quality of reconstructed HDR images, especially in the presence of motion.

Main Methods:

Introduced MSANLnet, a multi-scale attention-guided non-local network for HDR reconstruction.
Employed multi-scale spatial attention modules for implicit alignment of LDR image features.
Utilized non-local means-based fusion to reconstruct pixel intensity values by leveraging long-range dependencies.

Main Results:

MSANLnet demonstrated superior performance compared to state-of-the-art methods in quantitative evaluations.
Visual results confirmed the method's effectiveness in restoring saturated information and reducing ghosting artifacts caused by significant object movement.
Ablation studies validated the efficacy of the proposed architectural choices and modules.

Conclusions:

The proposed MSANLnet effectively addresses ghosting artifacts in HDR image reconstruction.
The multi-scale attention and non-local fusion approach leads to significant improvements in HDR image quality and robustness.