Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network

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.

Artificial Intelligence for Multimedia Signal Processing

At the ImageNet Large Scale Visual Re-Conversion Challenge (ILSVRC), a 2012 global image recognition contest, the University of Toronto Supervision team led by Prof [...]

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.