Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms
Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …
decades, the progress made in image denoising has benefited from the improved modeling …
A cross transformer for image denoising
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …
obtain good performance in image denoising. However, how to obtain effective structural …
[HTML][HTML] Comprehensive overview of backpropagation algorithm for digital image denoising
Artificial ANNs (ANNs) are relatively new computational tools used in the development of
intelligent systems, some of which are inspired by biological ANNs, and have found …
intelligent systems, some of which are inspired by biological ANNs, and have found …
Cycleisp: Real image restoration via improved data synthesis
The availability of large-scale datasets has helped unleash the true potential of deep
convolutional neural networks (CNNs). However, for the single-image denoising problem …
convolutional neural networks (CNNs). However, for the single-image denoising problem …
Real image denoising with feature attention
Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …
Image denoising: The deep learning revolution and beyond—a survey paper
Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …
oldest and most studied problems in image processing. Extensive work over several …
FFDNet: Toward a fast and flexible solution for CNN-based image denoising
Due to the fast inference and good performance, discriminative learning methods have been
widely studied in image denoising. However, these methods mostly learn a specific model …
widely studied in image denoising. However, these methods mostly learn a specific model …
The perception-distortion tradeoff
Image restoration algorithms are typically evaluated by some distortion measure (eg PSNR,
SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this …
SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this …
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
The discriminative model learning for image denoising has been recently attracting
considerable attentions due to its favorable denoising performance. In this paper, we take …
considerable attentions due to its favorable denoising performance. In this paper, we take …
The little engine that could: Regularization by denoising (RED)
Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …