Работы, цитирующие эту работу

Работ: 6

Работа: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

1. Learning Graph-Convolutional Representations for Point Cloud Denoising

   Francesca Pistilli, Giulia Fracastoro, Diego Valsesia +1

   Препринт3D Shape Modeling and AnalysisLecture notes in computer science2020Цитирований: 0

   ABIABI:AkademIndex/openalex/2020.preprint.000007

2. Speckle2Void: Deep Self-Supervised SAR Despeckling With Blind-Spot Convolutional Neural Networks

   Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1

   ПрепринтImage and Signal Denoising MethodsIEEE Transactions on Geoscience and Remote Sensing2021Цитирований: 0

   ABIABI:AkademIndex/openalex/2021.preprint.000011

3. Permutation Invariance and Uncertainty in Multitemporal Image Super-Resolution

   Diego Valsesia, Enrico Magli

   ПрепринтAdvanced Image Processing TechniquesIEEE Transactions on Geoscience and Remote Sensing2021Цитирований: 0

   ABIABI:AkademIndex/openalex/2021.preprint.000103

4. Comparison of ARIMA and Deep Learning Models for Forecasting the Consumer Price Index in Uzbekistan: Using R Packages

   Odiljon Rikhsimbaev, Akram Ishnazarov, Sarvar Mamasoliyev

   СтатьяStock Market Forecasting Methods2023Цитирований: 0

   ABIABI:AkademIndex/openalex/2023.article.002477

5. comprehensive review of image super-resolution metrics: classical and AI-based approaches

   Mukhriddin Arabboev, Shohruh Begmatov, Mokhirjon Rikhsivoev +2

   СтатьяAdvanced Image Fusion TechniquesACTA IMEKO2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000125

6. USING A NEURAL NETWORK TO REMOVE NOISE FROM IMAGES

   Anvar Asatilloyevich Ravshanov

   СтатьяImage and Signal Denoising MethodsHimičeskaâ tehnologiâ. Kontrolʹ i upravlenie/Chemical Technology. Control and Management2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.007304