Seonjae Kim, Dongsan Jun*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651
- 27 September 2021
Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. More >
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