Open Access

ARTICLE

Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

Ruizhe Yu¹, Tingrui Yue³, Lei Liang^2,3,*, Zhisheng Gao^1,*

1 School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
2 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
3 Low Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China

* Corresponding Authors: Lei Liang. Email: ; Zhisheng Gao. Email:

(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua 2025, 82(3), 5239-5256. https://doi.org/10.32604/cmc.2025.059077

Received 27 September 2024; Accepted 10 December 2024; Issue published 06 March 2025

Abstract

In the PSP (Pressure-Sensitive Paint), image deblurring is essential due to factors such as prolonged camera exposure times and high model velocities, which can lead to significant image blurring. Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources. To address these issues, this study proposes a deep learning-based approach tailored for PSP image deblurring. Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries, the images captured under such conditions exhibit distinctive non-uniform motion blur, presenting challenges for standard deep learning models utilizing convolutional or attention-based techniques. In this paper, we introduce a novel deblurring architecture featuring multiple DAAM (Deformable Ack Attention Module). These modules provide enhanced flexibility for end-to-end deblurring, leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1 × 1 convolutions, subsequently reassembled to enhance performance. Furthermore, we incorporate a RSC (Residual Shortcut Convolution) module for initial feature processing, aimed at reducing redundant computations and improving the learning capacity for representative shallow features. To preserve critical spatial information during upsampling and downsampling, we replace conventional convolutions with wt (Haar wavelet downsampling) and dysample (Upsampling by Dynamic Sampling). This modification significantly enhances high-precision image reconstruction. By integrating these advanced modules within an encoder-decoder framework, we present the DFDNet (Deformable Fusion Deblurring Network) for image blur removal, providing robust technical support for subsequent PSP data analysis. Experimental evaluations on the FY dataset demonstrate the superior performance of our model, achieving competitive results on the GOPRO and HIDE datasets.

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Keywords

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