Rip currents detection based on Sentinel-1 dual-polarization SAR data

Zhang, Rui; Zhang, Jie; Zhang, Xi; Meng, Junmin

doi:10.1109/radar53847.2021.10028193

Cited by 1 publication

(1 citation statement)

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“…In addition to allowing the network model to explore its own model weights, the network model itself can also employ additional supervised learning similar to the soft mask to make it suitable for the task of interest. We introduce the VGG [41] to integrate additional supervision into a supervised learning framework seamlessly.…”

Section: Mdcnn-vggmentioning

confidence: 99%

Multi-Domain Rapid Enhancement Networks for Underwater Images

Zhao,

Lee

2023

Sensors

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Images captured during marine engineering operations suffer from color distortion and low contrast. Underwater image enhancement helps to alleviate these problems. Many deep learning models can infer multi-source data, where images with different perspectives exist from multiple sources. To this end, we propose a multichannel deep convolutional neural network (MDCNN) linked to a VGG that can target multi-source (multi-domain) underwater image enhancement. The designed MDCNN feeds data from different domains into separate channels and implements parameters by linking VGGs, which improves the domain adaptation of the model. In addition, to optimize performance, multi-domain image perception loss functions, multilabel soft edge loss for specific image enhancement tasks, pixel-level loss, and external monitoring loss for edge sharpness preprocessing are proposed. These loss functions are set to effectively enhance the structural and textural similarity of underwater images. A series of qualitative and quantitative experiments demonstrate that our model is superior to the state-of-the-art Shallow UWnet in terms of UIQM, and the performance evaluation conducted on different datasets increased by 0.11 on average.

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Section: Mdcnn-vggmentioning

confidence: 99%