Department of Computer Engineering, Dong-A University, Busan 49315, Korea.
Media Intelligence Laboratory Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea.
Sensors (Basel). 2022 Nov 3;22(21):8476. doi: 10.3390/s22218476.
With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence. As one of the convolutional neural network-based VSR methods, we propose a deformable convolution-based alignment network (DCAN) to generate scaled high-resolution sequences with quadruple the size of the low-resolution sequences. The proposed method consists of a feature extraction block, two different alignment blocks that use deformable convolution, and an up-sampling block. Experimental results show that the proposed DCAN achieved better performances in both the peak signal-to-noise ratio and structural similarity index measure than the compared methods. The proposed DCAN significantly reduces the network complexities, such as the number of network parameters, the total memory, and the inference speed, compared with the latest method.
随着传感器的发展，图像和视频处理已经发展到用于视觉传感领域。其中，视频超分辨率（VSR）旨在从低分辨率序列重建高分辨率序列。为了利用低分辨率序列中的连续上下文，VSR 学习低分辨率序列中多个帧的空间和时间特征。作为基于卷积神经网络的 VSR 方法之一，我们提出了一种基于可变形卷积的对齐网络（DCAN），以生成大小为低分辨率序列四倍的缩放高分辨率序列。所提出的方法由特征提取块、两个使用可变形卷积的不同对齐块和上采样块组成。实验结果表明，所提出的 DCAN 在峰值信噪比和结构相似性指数测量方面的性能均优于比较方法。与最新方法相比，所提出的 DCAN 显著降低了网络复杂度，例如网络参数数量、总内存和推理速度。
