Learning filter selection policies for interpretable image denoising in parametrised action space
Abstract The denoising of images is an important research direction in computer vision. We consider the image denoising task as an estimation problem of the filtering policy related to image features, which is different from end‐to‐end image mapping. Commonly used simple filters such as gaussian fil...
Main Authors: | Runtao Xi, Jiahao Lyu, Tian Ma, Kang Sun, Yu Zhang, XiaoLin Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-03-01
|
Series: | IET Image Processing |
Subjects: | |
Online Access: | https://doi.org/10.1049/ipr2.12997 |
Similar Items
-
Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition
by: Jiajun Guo, et al.
Published: (2024-01-01) -
DeepFake detection against adversarial examples based on D‐VAEGAN
by: Ping Chen, et al.
Published: (2024-02-01) -
Design of Approximate Bilateral Filters for Image Denoising on FPGAs
by: Fanny Spagnolo, et al.
Published: (2023-01-01) -
Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images
by: Elena Solovyeva, et al.
Published: (2022-09-01) -
Geodesic methods in computer vision and graphics /
by: Peyre, Gabriel
Published: (2010)