GIFM: An Image Restoration Method With Generalized Image Formation Model for Poor Visible Conditions

Liang, Zheng; Zhang, Weidong; Ruan, Rui; Zhuang, Pinghui; Li, Chongyi

doi:10.1109/tgrs.2022.3227548

Cited by 21 publications

(3 citation statements)

References 72 publications

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“…Researchers have utilized various methods to enhance the accuracy of image classification ( Ding et al., 2020 ; Ding et al., 2023 ). These methods include the use of hybrid convolutional networks ( Chen et al., 2020 ; Zhao et al., 2022a ; Zhao et al., 2022b ), innovative networks ( Sun et al., 2023 ; Zhang et al., 2023b ; Zhang et al., 2024b ), improving image resolution ( Paoletti et al., 2018 ; Liang et al., 2022 ), underwater image enhancement using different methods ( Li et al., 2019 ; Li et al., 2021 ), multimodal deep learning models ( Yao et al., 2023 ) and combining convolutional neural networks with hyperspectral images ( Cao et al., 2020 ; Zheng et al., 2020 ; Xi et al., 2022 ; Yao et al., 2022 ). Deep learning methods address the limitations of traditional approaches by automatically learning feature representations from raw data, eliminating the need for manual feature design.…”

Section: Related Workmentioning

confidence: 99%

Efficient residual network using hyperspectral images for corn variety identification

Li,

Zhai,

Zheng

et al. 2024

Front. Plant Sci.

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Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

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Section: Related Workmentioning

confidence: 99%

Efficient residual network using hyperspectral images for corn variety identification

Li,

Zhai,

Zheng

et al. 2024

Front. Plant Sci.

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“…Existing low-light cameras from companies such as Sony, Photonis, SiOnyx, and Texas Instruments use high-performance charge-coupled devices or complementary met-al-oxide-semiconductor [9] technology, professional low-light circuits, and filters as core components to improve low-light image quality. However, due to the demanding manufacturing process, complex technology and high price of these high-performance equipment, they have not been widely used at this stage [10]. As an alternative, the use of algorithms to perform illumination enhancement on low-illumination images provides greater flexibility [11].…”

Section: Introductionmentioning

confidence: 99%

MTIE-Net: Multi-technology fusion of low-light image enhancement network

Tao,

Wu,

et al. 2024

PLoS ONE

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Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.

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“…Over the past decade, deep learning technology has significantly advanced and widely applied in diverse computer vision and image processing tasks [1,2,3,4,5]. These approaches significantly bolster high-level visual task performance, including tasks such as object detection [6,7,8], object recognition [9,10,11], and semantic segmentation [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Delving Deeper Into Image Dehazing: A Survey

Li,

Chen

et al. 2023

IEEE Access

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Images captured under foggy or hazy weather conditions are affected by the scattering of atmospheric particles, resulting in decreased contrast and color variation, thereby limiting their practical applications. In recent years, deep learning methods showcase significant advancements in image dehazing. However, the complexity and degradation factors in hazy images challenge the generalization capacity of dehazing methods. This paper comprehensively reviews the recent developments in single-image dehazing techniques based on deep learning. From the perspectives of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), different models are introduced and classified into four categories: Encoder-Decoder, Multi-Module, Multi-Branch, and Dual-Generative Adversarial Networks. The robustness and effectiveness of deep learning models are analyzed by comparing their performance and model complexity on public datasets. Additionally, limitations of current benchmark datasets and evaluation metrics are identified, and unresolved issues and future research directions are discussed. Our efforts in this paper will serve as a comprehensive reference for future research and call for further development in deep learning-based image dehazing. INDEX TERMS Image dehazing, Deep learning, Convolutional neural networks (CNNs), Generative adversarial networks (GANs).

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GIFM: An Image Restoration Method With Generalized Image Formation Model for Poor Visible Conditions

Cited by 21 publications

References 72 publications

Efficient residual network using hyperspectral images for corn variety identification

Efficient residual network using hyperspectral images for corn variety identification

MTIE-Net: Multi-technology fusion of low-light image enhancement network

Delving Deeper Into Image Dehazing: A Survey

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