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Found 17 papers, 12 papers with code
A self-supervised CNN for image watermark removal
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal.
Perceptive self-supervised learning network for noisy image watermark removal
Specifically, mentioned paired watermark images are obtained in a self supervised way, and paired noisy images (i. e., noisy and reference images) are obtained in a supervised way.
A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution.
Image super-resolution via dynamic network
In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block.
Ranked #49 on
Image Super-Resolution
on Set14 - 4x upscaling
A cross Transformer for image denoising
To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes.
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
In addition, a Distributed Hierarchical Integration (DHI) algorithm is also designed for the global solution process.
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
Multi-stage image denoising with the wavelet transform
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
Image Super-resolution with An Enhanced Group Convolutional Neural Network
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Asymmetric CNN for image super-resolution
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution.
Lightweight image super-resolution with enhanced CNN
Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image.
Ranked #45 on
Image Super-Resolution
on Set14 - 4x upscaling
Designing and Training of A Dual CNN for Image Denoising
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
Image denoising using deep CNN with batch renormalization
In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet).
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising.
Deep Learning for Image Denoising: A Survey
Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing.
