• Publications
  • Influence
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising, and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance.
Learning Deep CNN Denoiser Prior for Image Restoration
Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance, and enjoys several desirable properties, including the ability to handle a wide range of noise levels effectively with a single network.
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Extensive experimental results show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Toward Convolutional Blind Denoising of Real Photographs
A convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs and a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet.
Multi-level Wavelet-CNN for Image Restoration
This paper presents a novel multi-level wavelet CNN model for better tradeoff between receptive field size and computational efficiency, and shows the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.
Deep Unfolding Network for Image Super-Resolution
This paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning- based methods.
Color demosaicking plays a key role in digital imaging with a color filter array. Most existing demosaicking methods are based on hand-crafted priors, which may exhibit unpleasant visual artifacts in