• Journal of Information Processing Systems
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• v.18 no.1
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• pp.12-25
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• 2022

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.549-556
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• 2008

To achieve flexible visual content adaption for multimedia communications, the ISO/IEC MPEG & ITU-T VCEG form the JVT to develop SVC amendment for the H.264/AVC standard. JVT uses inter-layer prediction as well as inter prediction and intra prediction that are provided in H.264/AVC to remove the redundancy among layers. The main goal consists of designing inter-layer prediction tools that enable the usage of as much as possible base layer information to improve the rate-distortion efficiency of the enhancement layer. But inter layer prediction causes the computational complexity to be increased. In this paper, we proposed an efficient residual prediction. In order to reduce the computational complexity while maintaining the high coding efficiency. The proposed residual prediction uses modified interpolation that is defined in H.264/AVC SVC.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.111-120
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• 2020

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.144-145
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• 2007

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.414-420
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• 2015

The quality of depth images is important in the 3D video system to represent complete 3D contents. However, the original depth image from a depth camera has a low resolution and a flickering problem which shows vibrating depth values in terms of temporal meaning. This problem causes an uncomfortable feeling when we look 3D contents. In order to solve a low resolution problem, we employ 3D warping and a depth weighted joint bilateral filter. A temporal mean filter can be applied to solve the flickering problem while we encounter a residual spectrum problem in the depth image. Thus, after classifying foreground andbackground regions, we use an upsampled depth image for a foreground region and temporal mean image for background region.Test results shows that the proposed method generates a time consistent depth video with a high resolution.