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- 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.
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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