no code implementations • 1 Apr 2024 • Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.
no code implementations • 4 Dec 2023 • Kangfu Mei, Luis Figueroa, Zhe Lin, Zhihong Ding, Scott Cohen, Vishal M. Patel
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images.
1 code implementation • 2 Oct 2023 • Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M. Patel, Peyman Milanfar
Our conditional-task learning and distillation approach outperforms previous distillation methods, achieving a new state-of-the-art in producing high-quality images with very few steps (e. g., 1-4) across multiple tasks, including super-resolution, text-guided image editing, and depth-to-image generation.
no code implementations • 24 May 2023 • Kangfu Mei, Mo Zhou, Vishal M. Patel
The model can be scaled to generate high-resolution data while unifying multiple modalities.
no code implementations • 14 Dec 2022 • Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel
The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.
1 code implementation • 1 Dec 2022 • Kangfu Mei, Vishal M. Patel
Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images.
Ranked #11 on Video Generation on UCF-101
1 code implementation • 24 Aug 2022 • Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
In recent years, various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed in the literature which attempt to remove the distortion in the image.
1 code implementation • 19 Jul 2022 • Kangfu Mei, Vishal M. Patel, Rui Huang
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains.
no code implementations • 19 Apr 2022 • Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
In this paper, we systematically evaluate the effectiveness of various turbulence simulation methods on image restoration.
no code implementations • 6 Apr 2022 • Kangfu Mei, Yiqun Mei, Vishal M. Patel
In this paper, we first investigate the problem with a turbulence simulation method on real-world thermal images.
no code implementations • 4 Dec 2021 • Kangfu Mei, Vishal M. Patel
To mitigate the turbulence effect, in this paper, we propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN.
1 code implementation • 2 Apr 2021 • Kangfu Mei, Shenglong Ye, Rui Huang
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images.
1 code implementation • 10 Mar 2021 • Qi Song, Kangfu Mei, Rui Huang
In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics while keeping the efficiency high.
no code implementations • 5 Jan 2021 • Qiaosi Yi, Yunxing Liu, Aiwen Jiang, Juncheng Li, Kangfu Mei, Mingwen Wang
Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.
1 code implementation • 30 Aug 2020 • Juncheng Li, Faming Fang, Jiaqian Li, Kangfu Mei, Guixu Zhang
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
no code implementations • 24 Jun 2020 • Kangfu Mei, Yao Lu, Qiaosi Yi, Hao-Yu Wu, Juncheng Li, Rui Huang
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.
1 code implementation • 19 Nov 2019 • Kangfu Mei, Juncheng Li, Jiajie Zhang, Hao-Yu Wu, Jie Li, Rui Huang
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
1 code implementation • 4 Oct 2018 • Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang
Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.
1 code implementation • 3 Oct 2018 • Kangfu Mei, Aiwen Jiang, Juncheng Li, Jihua Ye, Mingwen Wang
Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.
1 code implementation • ECCV 2018 • Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).