Southwest University of Science and Technology Mianyang, China: May, 2019. Image segmentation using deep learning: A survey. Minaee S., Boykov Y., Porikli F., Plaza A., Kehtarnavaz N., Terzopoulos D. Deep learning for generic object detection: A survey. Liu L., Ouyang W., Wang X., Fieguth P., Chen J., Liu X., Pietikäinen M. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification Proceedings of the IEEE International Conference on Computer Vision Las Condes, Chile. The experimental research of the performance degradation in PDD CMOS image sensors induced by total ionizing dose radiation effects. The steel frame is anchored to the ground with heavy concrete blocks to prevent it from tipping over when subjected to heavy wind force. It is constructed with a steel frame where the blanket noise barrier is attached. Wang Z.J., Xue Y.Y., Ma W.Y., He B.P., Yao Z.B., Sheng J.K., Dong G.T. This is a very popular and typical mobile type temporary noise barrier wall used in many projects in Malaysia. Mish asymmetric convolution attention lightweight image denoising nuclear radiation scenes receptive field block texture retention. Compared with 12 denoising methods on our nuclear radiation dataset, the proposed method has the fewest model parameters, the highest quantitative metrics, and the best perceptual satisfaction, indicating its high denoising efficiency and rich texture retention. The entire network adopts a Mish activation function and asymmetric convolutions to improve the overall performance.
The TLU is at the bottom of the NLU and learns textures through an independent loss. Both the MKM and RM have receptive field blocks and attention blocks to enlarge receptive fields and enhance features. The NLU is bilinearly composed of a Multi-scale Kernel Module (MKM) and a Residual Module (RM), which learn non-local information and high-level features, respectively. In order to remove the strong noise with complex shapes and high density in nuclear radiation scenes, a lightweight network composed of a Noise Learning Unit (NLU) and Texture Learning Unit (TLU) was designed.
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