To address such a limitation, we design a novel deep learning dehazing model by combining the transformer and guided filter, which is called as Deep Guided Transformer Dehazing Network. Specially, we address the limitation of convolution via a transformer-based subnetwork, which can capture long ...
To address such a limitation, we design a novel deep learning dehazing model by combining the transformer and guided filter, which is called as Deep Guided Transformer Dehazing Network. Specially, we address the limitation of convolution via a transformer-based subnetwork, which can capture long ...
"Learning texture transformer network for image super-resolution." In CVPR, 2020. ⭐⭐⭐⭐ TransUnet: Chen, Jieneng, et al. "TransUnet: Transformers make strong encoders for medical image segmentation." arXiv preprint arXiv:2102.04306 (2021). ⭐⭐⭐⭐ Swin transformer: Liu, Z.,...
EEG-based anxiety emotion classification using an optimized convolutional neural network and transformer Article 16 April 2025 Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records Article Open access 02 May 2025 Enhanced diagnostics for generalized anxie...
[28] presented a U-shape Transformer that incorporates a channel-wise multi-scale feature fusion transformer module and a spatial-wise global feature modeling transformer module, specifically tailored for underwater image enhancement (UIE) tasks. This design strengthens the network's focus on color ...
[Dehazing] Color image dehazing using gradient channel prior and guided l0 filter, Information Sciences 2020. 1.4 Transformation as Prior Transforming image to different domain can bring favorable properties for network training e.g., some noise pattern are more apparent in certain frequency sub-bands...
Deep neural networks have been utilized in many computer vision tasks owing to their strong non-linear modeling ability. For dehazing regular images, Cai et al. (2016) proposed an end-to-end DehazeNet to estimate transmission maps. A multi-scale convolutional neural network (CNN) was applied to...
Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive re
Pan-sharpening with customized transformer and invertible neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7–14 February 2022; Volume 36, pp. 3553–3561. [Google Scholar] Beck, A.; Teboulle, M. A fast iterative shrinkage-thresholding ...
al. [116] introduced the Multi-scale Transformer Fusion Dehazing Network (MSTFDN), which utilized multi-scale Transformer blocks to capture long-range dependencies in image features. The inclusion of a feature enhancement module and a color restoration module ensured better image fidelity and ...