Infrared star image denoising using regions with deep reinforcement learning

Zhang, Zhenduo; Zheng, Wenbo; Ma, Zhanjun; Yin, Limei; Xie, Ming; Wu, Yirong

doi:10.1016/j.infrared.2021.103819

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…Scholars have found that image preprocessing methods based on super-resolution reconstruction technology [ 34 ] are particularly effective for classification improvement. Taking infrared image classification, which has gradually become a research hotspot since 2020, as an example, feature-based reconstruction methods emerged in 2021 [ 35 ], and models with covariant function regularization were proposed in 2022 [ 36 ]. However, the above processing operations mostly ignore the optimization of subsequent classification networks, and the design of quantitative indicators is not comprehensive enough.…”

Section: Related Research Progressmentioning

confidence: 99%

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics

Liu,

Wang,

Song

et al. 2024

Sensors

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Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.

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Section: Related Research Progressmentioning

confidence: 99%

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics

Liu,

Wang,

Song

et al. 2024

Sensors

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“…Xiao et al proposed a deep residual network architecture to remove noise from meteorological satellite infrared cloud images [ 30 ]. Zhang et al developed an infrared star image-denoising model in which an iterative denoising process is performed on the star area based on deep reinforcement learning [ 31 ]. Since the noise in terahertz imagery is difficult to model, the authors of ref.…”

Section: Related Workmentioning

confidence: 99%

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Guo,

Gao,

et al. 2023

Sensors

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Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.

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“…Then we use the strategy network, the feature map of the previous round as the input, and integrate the information of all frames to select the optimal region for analysis. Because the positioning process of video recognition is non differentiable, the process is trained by reinforcement learning algorithm [11][12]. Then, the local convolution neural network with large capacity and high accuracy is used to process only the patch selected by the strategy network [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient video classification of marine organisms through intra and inter prediction

Feng

Zhang

2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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The purpose of this paper is to improve the efficiency of video recognition process and save computational data. According to the encoding and decoding of video, we can know that not every part of the information of each frame is valid in video classification. There are a lot of invalid information, which takes up computing space. Therefore, this paper proposes an efficient video classification and prediction method based on reinforcement learning intra prediction. The process of eliminating time redundancy is further added, and then the video frames with low value are ignored, which further improves the computational efficiency. This method extracts the key frames from the marine biological video, and then focuses on the key image areas of the key frames, so as to reduce the network computing overhead.

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Infrared star image denoising using regions with deep reinforcement learning

Cited by 15 publications

References 20 publications

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Efficient video classification of marine organisms through intra and inter prediction

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