A Super-Resolution Network for Gastrointestinal Endoscopic Imaging via Adaptive Convolutional Feature Fusion

Super-resolution imaging in gastrointestinal endoscopy is essential for enhancing diagnostic precision and reducing the risk of misdiagnosis. Although many researchers have introduced super-resolution methods for endoscopic images, these methods treat all image regions uniformly, neglecting the complex spatial dependencies among pixels. To address this problem, this paper proposes an adaptive convolutional feature fusion network for the super-resolution of gastrointestinal endoscopic images. The proposed model comprises two main modules: the adaptive multiscale feature extraction module (AMFEM) and the convolutional feature fusion block (CFFB). The AMFEM integrates several key components: (1) multiscale feature projection, which provides diverse receptive fields; (2) hierarchical dilated pooling, which maintains structural consistency; (3) channel-wise feature refinement, which selectively enhances channel representations, improving the discrimination of critical diagnostic features; and (4) feature-oriented spatial attention, which preserves key anatomical structures and highlights crucial regions. The CFFB further enhances the feature extraction by integrating depthwise-separable and pointwise convolutions, which capture fine spatial and channel-wise details while reducing complexity. Experiments were conducted on the Kvasir gastrointestinal endoscopic images dataset. The quantitative and qualitative results indicate that the proposed model outperforms state-of-the-art methods, achieving superior PSNR and SSIM scores while preserving clinically significant features essential for accurate diagnosis.