• International Journal of Computer Science & Network Security
• /
• 제21권12spc호
• /
• pp.463-468
• /
• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.

• Journal of Information Processing Systems
• /
• 제18권1호
• /
• pp.12-25
• /
• 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 Institute of Control, Robotics and Systems
• /
• 제10권3호
• /
• pp.239-248
• /
• 2004

Optical coherence tomography(OCT) is high resolution imaging system which can see the cross section of microscopic organs in the living tissue. In this paper, we analyze the relation between the light source and the resolution of modulated signal in Michelson interferometer. We construct 1-D OCT signal processing hardware such as amplifiers, filters, and demodulate electronic signals from the photo detector. In order to get 2-D OCT image, the synchronization among optical delay line, sample stage and A/D converter is dealt with. In experiments, we verify analog and digital signal processing blocks which apply to the stacks of glasses. Finally we aquire high resolution 2-D OCT image with respect to the onion tissue. We expect that this result can be applied to the medical instrument through performance improvement.

• Journal of Korea Society of Digital Industry and Information Management
• /
• 제16권3호
• /
• pp.49-58
• /
• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• Journal of Broadcast Engineering
• /
• 제17권2호
• /
• pp.344-353
• /
• 2012

In this paper, we propose a super-resolution algorithm using discrete wavelet transform. In general super-resolution algorithms for single-image, probability based operations have been used for searching high-frequency components. Consequently, the complexity of the algorithm causes the increase of processing time. In the proposed algorithm, we use discrete wavelet transform to find high-frequency sub-bands. We perform inverse discrete wavelet transform using input image and high-frequency sub-bands of the same resolution as the input image which are obtained by performing discrete wavelet transform without down-sampling and then we obtain image with high-resolution. In the proposed algorithm, we use the down-sampled version of the original image ($512{\times}512$) as a test image ($256{\times}256$) to compare the performance of algorithms. Through experimental results, we confirm the improved efficiency of the proposed algorithm comparing with conventional interpolation algorithms and also decreased processing time comparing the probability based operations.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• 제38권2호
• /
• pp.169-178
• /
• 2001

The demand for high-resolution images is gradually increasing, whereas many imaging systems have been designed to allow a certain level of aliasing during image acquisition. Thus, digital image processing approaches have recently been investigated to reconstruct a high-resolution image from aliased low-resolution images. However, since the sub-pixel motion information is assumed to be accurate in most conventional approaches, the satisfactory high-resolution image cannot be obtained when the sub-pixel motion information is inaccurate. Therefore, in this paper we propose a new algorithm to reduce the distortion in the reconstructed high-resolution image due to the inaccuracy of sub-pixel motion information. For this purpose, we analyze the effect of inaccurate sub-pixel motion information on a high-resolution image reconstruction, and model it as zero-mean additive Gaussian errors added respectively to each low-resolution image. To reduce the distortion we apply the modified multi-channel image deconvolution approach to the problem. The validity of the proposed algorithm is both theoretically and experimentally demonstrated in this paper.

• Journal of applied mathematics & informatics
• /
• 제41권3호
• /
• pp.603-613
• /
• 2023

In this paper, the concept of geodesic centered tractography is explored for diffusion tensor imaging (DTI). In DTI, where geodesics has been tracked and the inverse of the fourth-order diffusion tensor is inured to determine the diversity. Specifically, we investigated geodesic tractography technique for High Angular Resolution Diffusion Imaging (HARDI). Riemannian geometry can be extended to a direction-dependent metric using Finsler geometry. Euler Lagrange geodesic calculations have been derived by Finsler geometry, which is expressed as HARDI in sixth order tensor.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제17권1호
• /
• pp.72-79
• /
• 2007

In this paper, It propose a new restoration algorithm of high resolution, which is reconstructed to high resolution image using low resolution image informations. The proposed algorithm is constructed based on super resolution theory, it is consisted of progressive steps of the integration and construction. It reduced a lot of data-processing capacity and noise with integration through sub-pixel movement and wavelet basis through a higher resolution. As a result, it is shown that the main information is maintained and the error rate is improved. Using expansion fuzzy wavelet B-spline interpolation in stage of construction, it is confirmed that we can achieve smoothing image and good resolution without blur and block.

• Proceedings of the KSRS Conference
• /
• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
• /
• pp.486-488
• /
• 2004

The SRI (Super Resolution Imager) uses the CCD (Charge coupled device) detector that is used to convert the light into electronic data. The purpose of the SRI is to obtain data for high resolution images by converting incoming light into digital stream of pixel data. The SRI has a high resolution, so this electronic system needs more fast imaging data processing, detector control and data transmission systems. This report describes the required system specification and manufactured electronic system for satellite.

• Korean Journal of Remote Sensing
• /
• 제28권6호
• /
• pp.683-691
• /
• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.