5 citations · 6 across the 5 of their papers we have counts for
5 papers
A Cosine Network for Image Super-Resolution
Chunwei Tian, Chengyuan Zhang, Bob Zhang +3
Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiv…
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…
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…