Image Denoising
416 papers with code • 19 benchmarks • 17 datasets
Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )
Benchmarks
These leaderboards are used to track progress in Image Denoising
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Libraries
Use these libraries to find Image Denoising models and implementations
FFHQ
BSD
Urban100
SIDD
SID
CBSD68
PolyU
DND
ELD
CRVD
Latest papers
Dual-domain strip attention for image restoration
c-yn/DSANet
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Neural Networks 2024
In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units.
Purified and Unified Steganographic Network
albblgb/pusnet
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It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network.
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
Masked Pre-trained Model Enables Universal Zero-shot Denoiser
krennic999/mpi
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In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for strong image denoising.
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
dichao-liu/anti-noise_fgvr
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The PMAL framework achieves high recognition accuracy by treating image denoising as an additional task in image recognition and progressively forcing a model to learn noise invariance.
Boosting of Implicit Neural Representation-based Image Denoiser
tids-lab/its
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Implicit Neural Representation (INR) has emerged as an effective method for unsupervised image denoising.
Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
lronkitty/ssrt
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This block consists of a spatial branch and a spectral branch.
NM-FlowGAN: Modeling sRGB Noise with a Hybrid Approach based on Normalizing Flows and Generative Adversarial Networks
In our experiments, our NM-FlowGAN outperforms other baselines on the sRGB noise synthesis task.
PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models
dennishein/cpfgmpp_pcct_denoising
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Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising.
AdaptIR: Parameter Efficient Multi-task Adaptation for Pre-trained Image Restoration Models
Recently, Parameter Efficient Transfer Learning (PETL) offers an efficient alternative solution to full fine-tuning, yet still faces great challenges for pre-trained image restoration models, due to the diversity of different degradations.
