82 citations · 253 across the 58 of their papers we have counts for
6 papers · 1 filter
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resol…
Multi-stage image denoising with the wavelet transform
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging d…
A heterogeneous group CNN for image super-resolution
Chunwei Tian, Yanning Zhang, Wangmeng Zuo +3
Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR)…
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution
Jiangtao Nie, Lei Zhang, Wei Wei +2
Despite the great success of deep model on Hyperspectral imagery (HSI) super-resolution(SR) for simulated data, most of them function unsatisfactory when applied to the real data,…
Attention-based network for low-light image enhancement
Cheng Zhang, Qingsen Yan, Yu zhu +3
The captured images under low light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vi…
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Dong Gong, Wei Sun, Qinfeng Shi +2
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods…