FDDCC-VSR: a lightweight video super-resolution network based on deformable 3D convolution and cheap convolution

Currently, the mainstream deep video super-resolution (VSR) models typically employ deeper neural network layers or larger receptive fields. This approach increases computational requirements, making network training difficult and inefficient. Therefore, this paper proposes a VSR model called fusion of deformable 3D convolution and cheap convolution (FDDCC-VSR).In FDDCC-VSR, we first divide the detailed features of each frame in VSR into dynamic features of visual moving objects and details of static backgrounds. This division allows for the use of fewer specialized convolutions in feature extraction, resulting in a lightweight network that is easier to train. Furthermore, FDDCC-VSR incorporates multiple D-C CRBs (Convolutional Residual Blocks), which establish a lightweight spatial attention mechanism to aid deformable 3D convolution. This enables the model to focus on learning the corresponding feature details. Finally, we employ an improved bicubic interpolation combined with subpixel techniques to enhance the PSNR (Peak Signal-to-Noise Ratio) value of the original image. Detailed experiments demonstrate that FDDCC-VSR outperforms the most advanced algorithms in terms of both subjective visual effects and objective evaluation criteria. Additionally, our model exhibits a small parameter and calculation overhead.