Abstract
Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.
Highlights
Sensors 2021, 21, 3351. https://Real-time object detection techniques have been applied to a variety of computer vision areas [1,2], such as object classification or object segmentation
We propose a lightweight convolutional neural network (CNN)-based Super resolution (SR) model to reduce the memory capacity as well as the network parameters
We propose multi-scale channel dense block (MCDB) to design the CNN based lightweight SR network structure
Summary
Real-time object detection techniques have been applied to a variety of computer vision areas [1,2], such as object classification or object segmentation Since it is mainly operated on the constrained environments, input images obtained from those environments can be deteriorated by camera noises or compression artifacts [3,4,5]. Super resolution (SR) method aims at recovering a high-resolution (HR) image from a low-resolution (LR) image. It is primarily deployed on the various image enhancement areas, such as the preprocessing for object detection [6] of Figure 1, medical images [7,8], satellite images [9], and surveillance images [10]. Convolutional neural network (CNN) [12] based
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