1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
A cross Transformer for image denoising
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural informa…
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…