3 citations · 4 across the 7 of their papers we have counts for
7 papers
Application of convolutional neural networks in image super-resolution
Chunwei Tian, Mingjian Song, Wangmeng Zuo +3
Due to strong learning abilities of convolutional neural networks (CNNs), they have become mainstream methods for image super-resolution. However, there are big differences of diff…
A Dynamic Transformer Network for Vehicle Detection
Chunwei Tian, Kai Liu, Bob Zhang +3
Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However,…
A Tree-guided CNN for image super-resolution
Chunwei Tian, Mingjian Song, Xiaopeng Fan +3
Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not…
Heterogeneous window transformer for image denoising
Chunwei Tian, Menghua Zheng, Chia-Wen Lin +2
Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue…
A self-supervised CNN for image watermark removal
Chunwei Tian, Menghua Zheng, Tiancai Jiao +3
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real…
Perceptive self-supervised learning network for noisy image watermark removal
Chunwei Tian, Menghua Zheng, Bo Li +3
Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the rea…
Adaptive Convolutional Neural Network for Image Super-resolution
Ziang Wu, Jinwei Xie, Xuanyu Zhang +4
Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenge…