Results 71 to 80 of about 6,876,175 (322)
Image Denoising Algorithm Based on Gradient Domain Guided Filtering and NSST
IEEE Access, 2023 Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods.
Zhe Li +3 more
doaj +1 more source
Denoising diffusion probabilistic models for 3D medical image generation
Scientific Reports, 2023 Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion.
Firas Khader +14 more
semanticscholar +1 more source
Deep Graph-Convolutional Image Denoising [PDF]
IEEE Transactions on Image Processing, 2020 Non-local self-similarity is well-known to be an effective prior for the image denoising problem. However, little work has been done to incorporate it in convolutional neural networks, which surpass non-local model-based methods despite only exploiting local information. In this paper, we propose a novel end-to-end trainable neural network architecture
Valsesia D., Fracastoro G., Magli E.
openaire +3 more sources
IEEE Access, 2020 Removal Poisson noise poses a very challenging technical issue because it is difficult to capture noise characteristics. This induces from the fact that Poisson noises from different sources affect each image pixel proportional to the pixel level.
Wuttipong Kumwilaisak +3 more
doaj +1 more source
Applied Sciences, 2023 Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character.
Juan Chen +5 more
doaj +1 more source
An ELU Network with Total Variation for Image Denoising
, 2017 In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function.
K Dabov, K Zhang, Y Chen, Y Wang
core +1 more source
Real Image Denoising With Feature Attention [PDF]
IEEE International Conference on Computer Vision, 2019 Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling.
Saeed Anwar, Nick Barnes
semanticscholar +1 more source
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Wavelet and Wavelet Packet Analysis For Image Denoising [PDF]
Engineering and Technology Journal, 2009 The denoising method based on wavelet or wavelet packet is used widely for image denoising. It is one of the most popular methods that depends on thresholding the wavelet coefficients using the Soft threshold.
Aymen Dawood Salman
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano +11 more
wiley +1 more source