Double enhanced residual network for biological image denoising.
Gene Expr Patterns
; 45: 119270, 2022 09.
Article
en En
| MEDLINE
| ID: mdl-36028213
ABSTRACT
With the achievements of deep learning, applications of deep convolutional neural networks for the image denoising problem have been widely studied. However, these methods are typically limited by GPU in terms of network layers and other aspects. This paper proposes a multi-level network that can efficiently utilize GPU memory, named Double Enhanced Residual Network (DERNet), for biological-image denoising. The network consists of two sub-networks, and U-Net inspires the basic structure. For each sub-network, the encoder-decoder hierarchical structure is used for down-scaling and up-scaling feature maps so that GPU can yield large receptive fields. In the encoder process, the convolution layers are used for down-sampling to obtain image information, and residual blocks are superimposed for preliminary feature extraction. In the operation of the decoder, transposed convolution layers have the capability to up-sampling and combine with the Residual Dense Instance Normalization (RDIN) block that we propose, extract deep features and restore image details. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm.
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Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes Neurales de la Computación
Tipo de estudio:
Qualitative_research
Idioma:
En
Revista:
Gene Expr Patterns
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2022
Tipo del documento:
Article