Rain Removal from a Single Image Using Refined Inception ResNet v2

Das, Bijit Kumar; Saha, Ayan; Mukhopadhyay, Sudipta

doi:10.1007/s00034-022-02279-x

Cited by 11 publications

(6 citation statements)

References 51 publications

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“…The inclusion of SE blocks facilitates dynamic channel-wise recalibration, augmenting the model's acuity in recognizing essential characteristics that distinguish various pest species [42]. Inception modules enhance the model's adaptability in managing diverse spatial dimensions, allowing the effective analysis of detailed pest imagery in natural settings [43]. The design of this model is particularly apt for detecting specific arboreal pests from the IP102 dataset, where the visual resemblances among different species present a notable classification challenge.…”

Section: Se-inception-resnet-v3 Modelmentioning

confidence: 99%

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An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

Li,

Tian,

Tian

et al. 2024

Agronomy

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The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection.

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Section: Se-inception-resnet-v3 Modelmentioning

confidence: 99%

“…the effective analysis of detailed pest imagery in natural settings [43]. The design of this model is particularly apt for detecting specific arboreal pests from the IP102 dataset, where the visual resemblances among different species present a notable classification challenge.…”

Section: Se-inception-resnet-v3 Modelmentioning

confidence: 99%

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

Li,

Tian,

Tian

et al. 2024

Agronomy

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“…Das, B. et al 45 suggested combining image decomposition and the Inception ResNet v2 (IR v2) network for rain removal. The rainy image is divided into base and detail layers by a guided filter.…”

Section: Related Workmentioning

confidence: 99%

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure.

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“…In this study, it introduces the Inception-RegNet model, a novel fusion aimed at enhancing the extraction of spectral-spatial features from high-resolution wetland images [39]. This hybrid model harnesses the multi-level feature extraction capabilities of Inception and the scalable architecture of RegNet, facilitating a sophisticated interpretation of intricate wetland attributes [40]. The adoption of this cohesive strategy is informed by its capacity to amalgamate the virtues of both foundational models synergistically, enhancing the precision and intricacy of wetland image segmentation.…”

Section: Inception-regnet Modelmentioning

confidence: 99%

A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

Li,

Cui,

Tian

2023

Applied Sciences

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Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.

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Rain Removal from a Single Image Using Refined Inception ResNet v2

Cited by 11 publications

References 51 publications

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

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