Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency.

Umirzakova, Sabina; Mardieva, Sevara; Muksimova, Shakhnoza; Ahmad, Shabir; Whangbo, Taegkeun

Umirzakova, Sabina; Mardieva, Sevara; Muksimova, Shakhnoza; Ahmad, Shabir; Whangbo, Taegkeun.

Afiliación

Umirzakova S; Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 113-120, Gyonggi-do, Republic of Korea.
Mardieva S; Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 113-120, Gyonggi-do, Republic of Korea.
Muksimova S; Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 113-120, Gyonggi-do, Republic of Korea.
Ahmad S; Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 113-120, Gyonggi-do, Republic of Korea.
Whangbo T; Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 113-120, Gyonggi-do, Republic of Korea.

Bioengineering (Basel) ; 10(11)2023 Nov 19.

Article en En | MEDLINE | ID: mdl-38002456

Palabras clave

RFDN; channel attention; medical image super-resolution; memory

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2023 Tipo del documento: Article Pais de publicación: Suiza

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