• Title, Summary, Keyword: Resolution

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A Novel Algorithm for Face Recognition From Very Low Resolution Images

  • Senthilsingh, C.;Manikandan, M.
    • Journal of Electrical Engineering and Technology
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    • v.10 no.2
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    • pp.659-669
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    • 2015
  • Face Recognition assumes much significance in the context of security based application. Normally, high resolution images offer more details about the image and recognizing a face from a reasonably high resolution image would be easier when compared to recognizing images from very low resolution images. This paper addresses the problem of recognizing faces from a very low resolution image whose size is as low as $8{\times}8$. With the use of CCTV(Closed Circuit Television) and with other surveillance camera-based application for security purposes, the need to overcome the shortcomings with very low resolution images has been on the rise. The present day face recognition algorithms could not provide adequate performance when employed to recognize images from VLR images. Existing methods use super-resolution (SR) methods and Relation Based Super Resolution methods to construct from very low resolution images. This paper uses a learning based super resolution method to extract and construct images from very low resolution images. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.

Consecutive-Frame Super-Resolution considering Moving Object Region

  • Cho, Sung Min;Jeong, Woo Jin;Jang, Kyung Hyun;Choi, Byung In;Moon, Young Shik
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.3
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    • pp.45-51
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    • 2017
  • In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

Stage-GAN with Semantic Maps for Large-scale Image Super-resolution

  • Wei, Zhensong;Bai, Huihui;Zhao, Yao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.8
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    • pp.3942-3961
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    • 2019
  • Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

An Effective Viewport Resolution Scaling Technique to Reduce the Power Consumption in Mobile GPUs

  • Hwang, Imjae;Kwon, Hyuck-Joo;Chang, Ji-Hye;Lim, Yeongkyu;Kim, Cheong Ghil;Park, Woo-Chan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.8
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    • pp.3918-3934
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    • 2017
  • This paper presents a viewport resolution scaling technique to reduce power consumption in mobile graphic processing units (GPUs). This technique controls the rendering resolution of applications in proportion to the resolution factor. In the mobile environment, it is essential to find an effective resolution factor to achieve low power consumption because both the resolution and power consumption of a GPU are in mutual trade-off. This paper presents a resolution factor that can minimize image quality degradation and gain power reduction. For this purpose, software and hardware viewport resolution scaling techniques are applied in the Android environment. Then, the correlation between image quality and power consumption is analyzed according to the resolution factor by conducting a benchmark analysis in the real commercial environment. Experimental results show that the power consumption decreased by 36.96% on average by the hardware viewport resolution scaling technique.

Super-Resolution Image Processing Algorithm Using Hybrid Up-sampling (하이브리드 업샘플링을 이용한 베이시안 초해상도 영상처리)

  • Park, Jong-Hyun;Kang, Moon-Gi
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.2
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    • pp.294-302
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    • 2008
  • In this paper, we present a new image up-sampling method which registers low resolution images to the high resolution grid when Bayesian super-resolution image processing is performed. The proposed up-sampling method interpolates high-resolution pixels using high-frequency data lying in all the low resolution images, instead of up-sampling each low resolution image separately. The interpolation is based on B-spline non-uniform re-sampling, adjusted for the super-resolution image processing. The experimental results demonstrate the effects when different up-sampling methods generally used such as zero-padding or bilinear interpolation are applied to the super-resolution image reconstruction. Then, we show that the proposed hybird up-sampling method generates high-resolution images more accurately than conventional methods with quantitative and qualitative assess measures.

Super Resolution Image Reconstruction using the Maximum A-Posteriori Method

  • Kwon Hyuk-Jong;Kim Byung-Guk
    • Proceedings of the KSRS Conference
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    • pp.115-118
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    • 2004
  • Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

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Super-resolution image enhancement by Papoulis-Gerchbergmethod improvement (Papoulis-Gerchberg 방법의 개선에 의한 초해상도 영상 화질 향상)

  • Jang, Hyo-Sik;Kim, Duk-Gyoo;Jung, Yoon-Soo;Lee, Tae-Gyoun;Won, Chul-Ho
    • Journal of Sensor Science and Technology
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    • v.19 no.2
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    • pp.118-123
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    • 2010
  • This paper proposes super-resolution reconstruction algorithm for image enhancement. Super-resolution reconstruction algorithms reconstruct a high-resolution image from multi-frame low-resolution images of a scene. Conventional super- resolution reconstruction algorithms are iterative back-projection(IBP), robust super-resolution(RS)method and standard Papoulis-Gerchberg(PG)method. However, traditional methods have some problems such as rotation and ringing. So, this paper proposes modified algorithm to improve the problem. Experimental results show that this proposed algorithm solve the problem. As a result, the proposed method showed an increase in the PSNR for traditional super-resolution reconstruction algorithms.

SUPER RESOLUTION RECONSTRUCTION FROM IMAGE SEQUENCE

  • Park Jae-Min;Kim Byung-Guk
    • Proceedings of the KSRS Conference
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    • pp.197-200
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    • 2005
  • Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper we applied super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

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COMPOUNDED METHOD FOR LAND COVERING CLASSIFICATION BASED ON MULTI-RESOLUTION SATELLITE DATA

  • HE WENJU;QIN HUA;SUN WEIDONG
    • Proceedings of the KSRS Conference
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    • pp.116-119
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    • 2005
  • As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

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High Resolution Reconstruction of Multispectral Imagery with Low Resolution (저해상도 Multispectral 영상의 고해상도 재구축)

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.23 no.6
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    • pp.547-552
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    • 2007
  • This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.