research-article

CBAM-DCE: A Non-Reference Image Correction Algorithm for Uneven Illumination

Authors:

Xianguang Kong,

Yijun SunAuthors Info & Claims

AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

Pages 803 - 809

https://doi.org/10.1145/3573942.3574098

Published: 16 May 2023 Publication History

Abstract

Affected by the change in daytime illumination sequence and by the shooting angle in the complex field environment, the kiwifruit images possess the unfriendly features of uneven illumination, such as local darkness and local brightness. The ill-posed image with uneven illumination will seriously constraint the subsequent image analysis processing. Current deep learning methods have achieved satisfactory results, and a large number of paired images (one is the input image, one is the ground truth image) is required to train the better network performance. However, it is difficult to capture ground truth images of the kiwifruit in the field. Based on this, the paper proposed Convolutional Block Attention Module Deep Curve Estimation (CBAM-DCE) to accomplish a non-reference illumination unevenness correction for field kiwifruit images. A deep learning network model is used to estimate the image-specific curve for image enhancement, and a non-reference loss function is applied to evaluate the image enhancement effect. Compared with seven related enhancement algorithms, the presented algorithm shakes off uneven illumination or normal-light image pairs for training. Five different public datasets and our Kiwifruit dataset were used in the experiments. Experiments demonstrate that our proposed CBAM-DCE is superior to other state-of-the-art algorithms for enhancing natural images under different lighting conditions.

References

[1]

Huang S, Wang Y, Su P. A New Synthetical Method of Feature Enhancement and Detection for SAR Image Targets[J]. Journal of Image and Graphics, 2016, 4(2): 73-77.

[2]

Veluchamy M, Subramani B. Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction[J]. Applied Soft Computing, 2020, 89: 106077.

Digital Library

[3]

Lew C J, Sim K S, Kho T K. Contrast and Brightness Enhancement for DICOM Images and Lesions Auto Detection[J]. Journal of Image and Graphics, 2016, 4(1).

[4]

Wang Q, Ward R K . Fast Image/Video Contrast Enhancement Based on Weighted. Thresholded Histogram Equalization[J]. IEEE Press, 2007.

Digital Library

[5]

Nakai K, Hoshi Y, Taguchi A. Color image contrast enhacement method based on differential intensity/saturation gray-levels histograms[C]. 2013 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, 2013: 445-449.

[6]

Veluchamy M, Subramani B. Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction[J]. Applied Soft Computing, 2020, 89: 106077.

Digital Library

[7]

Kaur K, Jindal N, Singh K. Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images[J]. Multimedia Tools and Applications, 2019, 78(19): 27891-27914.

Digital Library

[8]

Land E H, McCann J J. Lightness and retinex theory[J]. Josa, 1971, 61(1): 1-11.

[9]

Guo X, Li Y, Ling H. LIME: Low-light image enhancement via illumination map estimation[J]. IEEE Transactions on image processing, 2016, 26(2): 982-993.

Digital Library

[10]

Li M, Liu J, Yang W, Structure-revealing low-light image enhancement via robust retinex model[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2828-2841.

[11]

Ren W, Liu S, Ma L, Low-light image enhancement via a deep hybrid network[J]. IEEE Transactions on Image Processing, 2019, 28(9): 4364-4375.

[12]

Wei C, Wang W, Yang W, Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv:1808.04560, 2018.

[13]

Wang R, Zhang Q, Fu C W, Underexposed photo enhancement using deep illumination estimation[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 6849-6857.

[14]

Ignatov A, Kobyshev N, Timofte R, Dslr-quality photos on mobile devices with deep convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3277-3285.

[15]

Chen C, Chen Q, Xu J, Learning to see in the dark[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3291-3300.

[16]

Jiang Y, Gong X, Liu D, Enlightengan: Deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 2021, 30: 2340-2349.

Digital Library

[17]

Guo C, Li C, Guo J, Zero-reference deep curve estimation for low-light image enhancement[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1780-1789.

[18]

Li C, Guo C, Loy C C. Learning to enhance low-light image via zero-reference deep curve estimation[J]. arXiv preprint arXiv:2103.00860, 2021.

[19]

Ying Z, Li G, Gao W. A bio-inspired multi-exposure fusion framework for low-light image enhancement[J]. arXiv preprint arXiv:1711.00591, 2017.

[20]

Lv F, Liu B, Lu F. Fast enhancement for non-uniform illumination images using light-weight CNNs[C]. Proceedings of the 28th ACM International Conference on Multimedia. 2020: 1450-1458.

[21]

Afifi M, Derpanis K G, Ommer B, Learning multi-scale photo exposure correction[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 9157-9167.

[22]

Woo S, Park J, Lee J Y, Cbam: Convolutional block attention module[C]. Proceedings of the European conference on computer vision (ECCV). 2018: 3-19.

[23]

Cai J, Gu S, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.

[24]

Mertens T, Kautz J, Van Reeth F. Exposure fusion: A simple and practical alternative to high dynamic range photography[C]. Computer graphics forum. Oxford, UK: Blackwell Publishing Ltd, 2009, 28(1): 161-171.

[25]

Lee C, Lee C, Kim C S. Contrast enhancement based on layered difference representation[C]. 2012 19th IEEE international conference on image processing. IEEE, 2012: 965-968.

[26]

Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal processing letters, 2012, 20(3): 209-212.

[27]

Wang S, Zheng J, Hu H M, Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE transactions on image processing, 2013, 22(9): 3538-3548.

Digital Library

[28]

Lee C, Lee C, Lee Y Y, Power-constrained contrast enhancement for emissive displays based on histogram equalization[J]. IEEE transactions on image processing, 2011, 21(1): 80-93.

Cited By

Salas-Espinales AVázquez-Martín RGarcía-Cerezo AMandow A(2023)SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)10.1109/SSRR59696.2023.10499929(139-144)Online publication date: 13-Nov-2023
https://doi.org/10.1109/SSRR59696.2023.10499929

Index Terms

CBAM-DCE: A Non-Reference Image Correction Algorithm for Uneven Illumination
1. Networks
  1. Network performance evaluation
    1. Network experimentation

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AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

September 2022

1221 pages

ISBN:9781450396899

DOI:10.1145/3573942

Copyright © 2022 ACM.

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Published: 16 May 2023

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National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province

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AIPR 2022

AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

September 23 - 25, 2022

Xiamen, China

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Cited By

Salas-Espinales AVázquez-Martín RGarcía-Cerezo AMandow A(2023)SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)10.1109/SSRR59696.2023.10499929(139-144)Online publication date: 13-Nov-2023
https://doi.org/10.1109/SSRR59696.2023.10499929

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