CT image super-resolution reconstruction via pixel-attention feedback network

Abstract

Computed tomography (CT) imaging has been widely used in clinical medicine, and high-resolution CT images play a crucial role in the determination of lesions. To fully excavate the contributive information of initial features and improve the feature representation ability of the model, we propose a pixel-attention feedback network (PAFNet) for CT image super-resolution reconstruction. Specifically, the PAFNet adopts multi-feedback network as backbone to make full use of initial features. Subsequently, a gated feedback (GF) block is introduced to refine the underlying features using the feedback features. To enrich the output characteristics and pay attention to essential details, a pixel attention mechanism is adopted to the self-calibration convolution. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art approaches.