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- Shift-Net: Image Inpainting via Deep Feature Rearrangement511
A special shift-connection layer to the U-Net architecture, namely Shift-Net, is introduced for filling in missing regions of any shape with sharp structures and fine-detailed textures and an end-to-end learning algorithm is further developed to train the Shift- net.
- Blind Face Restoration via Deep Multi-scale Component Dictionaries230
A deep face dictionary network (termed as DFDNet) to guide the restoration process of degraded observations and can achieve plausible performance in both quantitative and qualitative evaluation, and can generate realistic and promising results on real degraded images without requiring an identity-belonging reference.
- Progressive Semantic-Aware Style Transformation for Blind Face Restoration206
A new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration, which makes full use of the semantic and pixel space information from different scales of input pairs and pretrain a face parsing network which can generate decent parsing maps from real-world LQ face images.
- Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors202
This work proposes Feature Matching SR (FeMaSR), which restores realistic HR images in a much more compact feature space by matching distorted LR image features to their distortion-free HR counterparts in the authors' pretrained HR priors, and decoding the matched features to obtain realisticHR images.
- A comparison of empirical and model-driven optimization202
The experiments show that model-driven optimization can be surprisingly effective, and can generate code whose performance is comparable to that of code generated by empirical optimizers for the BLAS.
- Learning Warped Guidance for Blind Face Restoration178
Experiments show that the GFRNet not only performs favorably against the state-of-the-art image and face restoration methods, but also generates visually photo-realistic results on real degraded facial images.
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- Face Super-Resolution Guided by 3D Facial Priors89
This paper proposes a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures and is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes.
- Learning Semantic Person Image Generation by Region-Adaptive Normalization88
This work proposes a new two-stage framework to handle the pose and appearance translation and suggests a new person image generation method by incorporating the region-adaptive normalization, in which it takes the per-region styles to guide the target appearance generation.
- Performance and model of a full-scale up-flow anaerobic sludge blanket (UASB) to treat the pharmaceutical wastewater containing 6-APA and amoxicillin85
The results indicated that these models built on the BPNN theory were well-fitted to the detected data, and were able to simulate and predict the removal of COD, 6-APA and amoxicillin by UASB.
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- Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM72
A method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM) is proposed to overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity.
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- Learning Dual Memory Dictionaries for Blind Face Restoration63
This paper suggests a DMDNet by explicitly memorizing the generic and specific features through dual dictionaries, which is optimized in an end-to-end manner and can be flexibly plugged into different application scenarios.
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- Algorithmic Management for Improving Collective Productivity in Crowdsourcing54
The surprise-minimization-value-maximization (SMVM) approach is proposed, which established a simple and novel worker desirability index (WDI) jointly considering the effect of each worker’s reputation, workload and motivation to work on collective productivity.
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- SIFT Based Automatic Registration of Remotely-sensed Imagery46
SIFT (Scale Invariant Feature Transform) feature, which has shown great success in computer vision, is introduced into image registration in remote sensing and a feature matching approach based on the specific characteristic of the remote sensing imagery is proposed.
- Learning Generative Structure Prior for Blind Text Image Super-resolution42
This work learns to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration and stores the discrete features for each character in a codebook to restrict the generative space of StyleGAN.
- When StyleGAN Meets Stable Diffusion: a $\mathcal{W}_{+}$ Adapter for Personalized Image Generation36
The novel use of the extended StyleGAN embed-ding space $\mathcal{W}+$, to achieve enhanced identity preservation and disentanglement for diffusion models, and proposes new training objectives to balance the influences of both prompt and identity conditions.
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- Learning Symmetry Consistent Deep CNNs for Face Completion31
Two kinds of symmetry-enforcing modules are Leveraged to form a symmetry-consistent CNN model (i.e., SymmFCNet) for effective face completion and can generate globally consistent results on images with synthetic and real occlusions, and performs favorably against state-of-the-arts.
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- Survey on Deep Face Restoration: From Non-blind to Blind and Beyond26
The prevalent factors responsible for real-world LQ images and degradation techniques used to synthesize LQ images are examined and degradation techniques used to synthesize LQ images are introduced.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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