A two-fold fusion fuzzy framework to restore non-uniform illuminated blurred image

Chaki, Jyotismita

doi:10.1016/j.ijleo.2020.164197

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Also, the addition of the Wavelets and color guidance block was impressive [1]. [4] used a two-stage fusion to improve processing speed. However, they all had a certain degree of performance deficiencies.…”

Section: Related Work a Lightweight Super-resolution Networkmentioning

confidence: 99%

Lightweight Image Super-Resolution With Mobile Share-Source Network

Wei

Fan

et al. 2020

IEEE Access

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Within the development of the deep convolutional neural network, the great achievements had been made in the single-image super-resolution (SISR) task. However, the higher performance always comes with the deeper layers which also brings larger numbers of network operations and parameters that make it hard to implement in practice. In our work, a lightly super-resolution, named Mobile Share-Source Network (MSSN), is purposed to address these practical issues. In MSSN, a high-efficiency block, the mobile adaptive weighted residual unit, is designed to fulfill the need for the reduction in both parameters and the Mult-Adds while maintaining the performance with importing the deep separable convolution. Moreover, it brings into the Adaptive Weighted Share-Source Skip Connection, getting abundant information from the shallow layer which helps reconstruct better images. The experimental results show that our network has fewer numbers of parameters and operations than the state-of-the-art lightweight network while maintaining high reconstruction quality comparing with many state-of-the-art super-resolution methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM).

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Section: Related Work a Lightweight Super-resolution Networkmentioning

confidence: 99%

Lightweight Image Super-Resolution With Mobile Share-Source Network

Wei

Fan

et al. 2020

IEEE Access

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show abstract

“…Te light spot problem encountered in this task is similar to the shadow phenomenon [6][7][8] in text image processing. In addition to traditional image optimization methods [9][10][11][12], deep learning (DL) has received immense attention in the past few years. As one of the most popular models in deep learning, generative adversarial network (GAN)-based models [13,14] have been widely used in image restoration.…”

Section: Introductionmentioning

confidence: 99%

Bridge Displacement Measurement Using the GAN-Network-Based Spot Removal Algorithm and the SR-Based Coarse-to-Fine Target Location Method

Yu,

Zhang

2023

Structural Control and Health Monitoring

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Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement.

show abstract

A two-fold fusion fuzzy framework to restore non-uniform illuminated blurred image

Cited by 2 publications

References 21 publications

Lightweight Image Super-Resolution With Mobile Share-Source Network

Lightweight Image Super-Resolution With Mobile Share-Source Network

Bridge Displacement Measurement Using the GAN-Network-Based Spot Removal Algorithm and the SR-Based Coarse-to-Fine Target Location Method

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