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Image Denoising
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Autoregressive Score Matching
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Learning Integrodifferential Models for Image Denoising
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN
Unrolling of Deep Graph Total Variation for Image Denoising
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
Practical Deep Raw Image Denoising on Mobile Devices
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser
Alternating minimization for a single step TV-Stokes model for image denoising
Iterative regularization algorithms for image denoising with the TV-Stokes model
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties
Independent finite approximations for Bayesian nonparametric inference
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
Enhancing and Learning Denoiser without Clean Reference
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Extensions to the Proximal Distance Method of Constrained Optimization
Operational vs Convolutional Neural Networks for Image Denoising
Unpaired Learning of Deep Image Denoising
Deep Variational Network Toward Blind Image Restoration
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms
Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Efficient Blind-Spot Neural Network Architecture for Image Denoising
Coupling BM3D with directional wavelet packets for image denoising
PNEN: Pyramid Non-Local Enhanced Networks
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Unsupervised Image Restoration Using Partially Linear Denoisers
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise
Depth image denoising using nuclear norm and learning graph model
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