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Found 9 papers, 4 papers with code
Textual Prompt Guided Image Restoration
In this paper, an effective textual prompt guided image restoration model has been proposed.
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting
Besides the lightweight models, we also show that the suggested review mechanism can be used as a plug-and-play module to further boost the performance of a kind of heavy crowd counting models without modifying the neural network architecture and introducing any additional model parameter.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Contrastive Learning for Local and Global Learning MRI Reconstruction
Therefore, we propose a Spatial and Fourier Layer (SFL) to simultaneously learn the local and global information in Spatial and Fourier domains.
Structure-Preserving Deraining with Residue Channel Prior Guidance
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.
Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background
Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.
Disentangle Perceptual Learning through Online Contrastive Learning
Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.
