C-SwinIR: Dental CT super-resolution reconstruction fused with SwinIR

Abstract

Super-resolution reconstruction (SR) of dental computed tomography (Dental CT) images is a innovative and challenging task. To address the limitations of Dental CT in obtaining high-resolution (HR) images due to equipment constraints and noise interference, we propose a Dental CT SR method called C-SwinIR based on SwinIR. Firstly, the self-calibrated convolutions network (SCNet) is introduced to solve the problem of detail loss in shallow feature graphs and improve the ability to recover details. Subsequently, the cross-shaped windows (CSWin) self-attention transformer structure is used to replace the original transformer structure, which improves the ability of the model to obtain context information. Eventually, the integration of efficient channel attention (ECA-Net) module effectively realizes the local cross-channel interaction, accelerates the model convergence and solves the problem of gradient explosion in training. Experimental results show that our proposed method is superior to the original SwinIR network by achieving a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) by 0.287 and 0.003 respectively. Additionally, it decreases mean squared error (MSE) by 1.108. By using this method, clinicians can obtain clearer details and textures from Dental CT, which effectively assist in diagnosis.

Improving resolution of panoramic radiographs: super-resolution concept

“…Wang et al used 2400 dental computed tomography images for deep learning model. 15 High-resolution images were firstly sampled 4 times by the Bicubic interpolation method. PSNR and SSIM were reported as 36.319 and 0.882.…”

“… 4 Wang et al used deep learning model to provide clearer details and textures from Dental CT and facilitate effective diagnosis. 15 Li et al investigated a super-resolution model using panoramic radiographs for predicting mandibular third molar extraction difficulty. 16 Hatvaniy et al adopted single image super-resolution (SISR) technique by employing Tucker decomposition for denoising and deconvolution of 3D dental CT images.…”