Vision transformers for dense prediction: A survey
S Zuo, Y Xiao, X Chang, X Wang - Knowledge-Based Systems, 2022 - Elsevier
Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …
Survey on rain removal from videos or a single image
Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …
exploration of rain removal from videos or a single image has drawn considerable attention …
Image de-raining transformer
Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …
architectures. However, the intrinsic limitations of convolution, including local receptive fields …
Sparse gradient regularized deep retinex network for robust low-light image enhancement
Due to the absence of a desirable objective for low-light image enhancement, previous data-
driven methods may provide undesirable enhanced results including amplified noise …
driven methods may provide undesirable enhanced results including amplified noise …
All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss
WT Chen, HY Fang, CL Hsieh… - Proceedings of the …, 2021 - openaccess.thecvf.com
Snow is a highly complicated atmospheric phenomenon that usually contains snowflake,
snow streak, and veiling effect (similar to the haze or the mist). In this literature, we propose …
snow streak, and veiling effect (similar to the haze or the mist). In this literature, we propose …
Spatially-adaptive image restoration using distortion-guided networks
We present a general learning-based solution for restoring images suffering from spatially-
varying degradations. Prior approaches are typically degradation-specific and employ the …
varying degradations. Prior approaches are typically degradation-specific and employ the …
Density-aware single image de-raining using a multi-stream dense network
Single image rain streak removal is an extremely challenging problem due to the presence
of non-uniform rain densities in images. We present a novel density-aware multi-stream …
of non-uniform rain densities in images. We present a novel density-aware multi-stream …
Deep multi-scale convolutional neural network for dynamic scene deblurring
Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision
problem as blurs arise not only from multiple object motions but also from camera shake …
problem as blurs arise not only from multiple object motions but also from camera shake …
Attentive generative adversarial network for raindrop removal from a single image
Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a
background scene and degrade an image considerably. In this paper, we address the …
background scene and degrade an image considerably. In this paper, we address the …
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Recently, very deep convolutional neural networks (CNNs) have shown outstanding
performance in object recognition and have also been the first choice for dense …
performance in object recognition and have also been the first choice for dense …