Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks229 from public data
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising8,921
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.
- SwinIR: Image Restoration Using Swin Transformer5,285
A strong baseline model SwinIR is proposed for image restoration based on the Swin Transformer that outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced byUp to 67%.
- Learning Deep CNN Denoiser Prior for Image Restoration2,156
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.
- 1,955
- Plug-and-Play Image Restoration With Deep Denoiser Prior1,296
Experimental results demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state- of theart learning- based methods.
- Toward Convolutional Blind Denoising of Real Photographs1,145
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.
- Learning a Single Convolutional Super-Resolution Network for Multiple Degradations1,121
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.
- Designing a Practical Degradation Model for Deep Blind Image Super-Resolution1,035
The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications.
- Multi-level Wavelet-CNN for Image Restoration932
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.
- 619
- Denoising Diffusion Models for Plug-and-Play Image Restoration511
Experimental results on three representative IR tasks demonstrate that DiffPIR achieves state-of-the-art performance on both the FFHQ and ImageNet datasets in terms of reconstruction faithfulness and perceptual quality with no more than 100 NFEs.
- 487
- PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting470
PhySG is presented, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images, and demonstrates, with both synthetic and real data, that it can enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
- LocalViT: Analyzing Locality in Vision Transformers460
The locality mechanism is systematically investigated by carefully designed controlled experiments and the same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept.
- VRT: A Video Restoration Transformer448
Experimental results on five tasks, including video super-resolution, video deblurring, video denoising, video frame interpolation and space-time video super-resolution, demonstrate that VRT outperforms the state-of-the-art methods by large margins on fourteen benchmark datasets.
- Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels406
Quantitative and qualitative evaluations on synthetic and real LR images demonstrate that the proposed deep plug-and-play super-resolution framework is flexible and effective to deal with blurry LR images.
- 360
- Neural Blind Deconvolution Using Deep Priors347
Experimental results show that the proposed SelfDeblur can achieve notable quantitative gains as well as more visually plausible deblurring results in comparison to state-of-the-art blind deconvolution methods on benchmark datasets and real-world blurry images.
- 307
- Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis270
Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.
- Outlier-robust extreme learning machine for regression problems240
This work proposes an outlier-robust ELM where the l 1 -norm loss function is used to enhance the robustness and the fast and accurate augmented Lagrangian multiplier method is applied to guarantee the effectiveness and efficiency.
- 228
- Flow-based Kernel Prior with Application to Blind Super-Resolution160
This paper proposes a normalizing flow-based kernel prior (FKP) that optimizes the kernel in the latent space rather than the network parameter space, which allows it to generate reasonable kernel initialization, traverse the learned kernel manifold and improve the optimization stability.
- IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images157
This work proposes a neural inverse rendering pipeline that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines and achieves significantly better inverse rendering quality compared to prior works.
- DHP: Differentiable Meta Pruning via HyperNetworks140
This paper introduces a differentiable pruning method via hypernetworks for automatic network pruning, and extensive experiments are conducted on various networks for image classification, single image super-resolution, and denoising.
- Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling131
The hierarchical conditional flow (HCFlow) is proposed as a unified framework for image SR and image rescaling and achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.
- 126
- CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution123
This paper explicitly design an implicit attention network to learn the ensemble weights for the nearby local features in a continuous implicit attention-in-attention network, called CiaoSR, which significantly outperforms the existing single image SR methods with the same backbone.
- 123
- Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution120
Extensive experiments show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
- 110
- Analysis of Gender Inequality In Face Recognition Accuracy104
It is shown that the accuracy difference persists even if a state-of-the-art deep learning method is trained from scratch using training data explicitly balanced between male and female images and subjects.
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
- Prototype vector machine for large scale semi-supervised learning100
This work proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale semi-supervised learning (SSL), with key innovation is the use of "prototypes vectors" for efficient approximation on both the graph- based regularizer and model representation.
- 96
- 95
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- A hybrid approach combining extreme learning machine and sparse representation for image classification89
A novel hybrid approach combining ELM and SRC for image classification is proposed and results demonstrate that it not only outperforms ELM in classification accuracy but also has much less computational complexity than SRC.
- Selecting the reference image for registration of CEST series81
To compare different reference images selected for registration among chemical exchange saturation transfer (CEST) series, a comparison study is conducted at the ULTIMATE level.
- How Does Gender Balance In Training Data Affect Face Recognition Accuracy?72
This work investigates female under-representation in the training data is truly the cause of lower accuracy for females on test data, and shows that gender balance in theTraining data does not translate into gender Balance in the test accuracy, and the “gender gap” in test accuracy is not minimized by a gender-balanced training set, but by a training set with more male images than female images.
- 69
- Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology65
This work presents the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result.
- 64
- AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results63
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
- 58
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
- Robustness of full implication algorithms based on interval-valued fuzzy inference51
A new full implication algorithm based on interval-valued fuzzy inference which extends the triple I principle for fuzzy inference based on fuzzymodus ponens and fuzzy modus tollens to the case of interval- valued fuzzy sets is presented.
- 50
- 49
- From Categorical to Numerical: Multiple Transitive Distance Learning and Embedding47
This paper proposes a novel method to transform categorical data to numerical representations, so that abundant numerical learning methods can be exploited in categoricalData mining.
- 46
- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
- 41
- 37
- 36
- 36
- 35
- Reduced Kernel Extreme Learning Machine34
The experimental results show that Reduced-KELM can perform at a similar level of accuracy as K ELM and at the same time being significantly faster than KELM.
- 31
- 30
- 29
- 27
- 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.
- 26
- 25
- 23
- 23
- 22
- The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures21
This paper investigates a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks, and finds that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance.
- 21
- 21
- 21
- 20
- 19
- 19
- 19
- 15
- 14
- 14
- 13
- Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction13
- 13
- 12
- 12
- 12
- 11
- 11
- 11
- 11
- 10
- Accelerating Video Diffusion Models via Distribution Matching10
This work introduces a novel framework for diffusion distillation and distribution matching that dramatically reduces the number of inference steps while maintaining-and potentially improving-generation quality.
- 10
- 10
- 10
- 9
- 9
- 9
- 9
- 8
- 8
- 7
- 7
- 7
- 7
- 7
- Towards Understanding Graphical Perception in Large Multimodal Models6
The theory of graphical perception, an approach used to study how humans decode visual information encoded on charts and graphs, is used to develop an evaluation framework for analyzing gaps in LMMs' perception abilities in charts, which underscores several critical limitations of current state-of-the-art LMMs, including GPT-4o.
- The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture.6
The ``heterogeneity hypothesis'' is articulate: with the same training protocol, there exists a layer-wise dissimilated network architecture (LW-DNA) that can outperform the original network with regular channel configurations under lower level of model complexity.
- 6
- 6
- 6
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- Toward Realistic Low-Light Image Enhancement via ISP-Driven Data Modeling–
This paper proposes an image signal processing (ISP)-driven data synthesis pipeline that addresses challenges by generating scalable paired training data and applies it to existing LLIE methods, demonstrating its impact on improving their practicality and generalizability in real-world applications.
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.