Results 41 to 50 of about 116,500 (328)
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
True 4D Image Denoising on the GPU
International Journal of Biomedical Imaging, 2011 The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data.
Anders Eklund +2 more
doaj +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
A Research and Strategy of Remote Sensing Image Denoising Algorithms
, 2019 Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground.
B Wen +9 more
core +1 more source
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
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
Iterative CT reconstruction using shearlet-based regularization [PDF]
, 2012 In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization
Goossens, Bart +6 more
core +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
No-reference Image Denoising Quality Assessment [PDF]
, 2018 A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting.
Lu, Si
core +2 more sources
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 more
wiley +1 more source