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Variational low-light image enhancement based on a haze model

IEIE Transactions on Smart processing and Computing 2018年第4期7卷 325-331页

作者： Shin, Joongchol Park, Hasil Park, Jinho Ha, Jinsol Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University / Seoul Korea Republic of Department of Integrative Engineering Chung-Ang University Seoul Korea Republic of

Under low-illumination conditions, an acquired image is degraded by a limited dynamic range and noise in signal amplification. To solve this problem, we propose a haze model-based variational low-light image-enhancement method. The proposed method includes two steps: i) estimation of the initial transmission map using block-based dark channel prior and a Gaussian pyramid, and ii) L2-norm regularized optimization based on the haze model. Experimental results show that the proposed enhancement method outperforms conventional state-of-the-art methods in terms of visible contrast without noise amplification. Copyrights © 2018 The Institute of Electronics and Information Engineers.

关键词： image enhancement

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Example-based super-resolution using self-patches and approximated constrained least squares filter

Example-based super-resolution using self-patches and approx...

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作者： Cho, Changhun Jeon, Jaehwan Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Republic of

ISBN: (纸本)9781479957514

This paper presents a novel super-resolution (SR) algorithm using local self-examples. The proposed algorithm consists of three steps: i) generation of the patch dictionary using multiple-step image blurring, ii) search of the optimum patches using the magnitude and orientation of the image gradient, and iii) combination of the restored and original patches for reducing the patch-mismatching error. Example-based SR methods have a common disadvantage of unnaturally reconstructed edges. The proposed method can reconstruct realistic images by searching patches based on the edge strength in dictionary made by multiple-step degradations. Experimental results show that the proposed SR algorithm provides more natural images with less synthetic artifacts than existing methods. The proposed SR method provides significant improvement in both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM). © 2014 IEEE.

关键词： image reconstruction

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Deep tracking using convolutional features and adaptive frame update 8

Deep tracking using convolutional features and adaptive fram...

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8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018

作者： Kim, Yeongbin Park, Hasil Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781538660959

This paper presents a robust tracking algorithm using convolutional features. The proposed tracking algorithm consists of three steps: I) training correlation filters using features extracted by a convolutional neural network, ii) localizing the target position in the next frame by multiplying weight and correlation response of each layer, and iii) re-training correlation filters every 10-frame using the proposed position error score. Experimental results show that the proposed algorithm outperforms an existing deep learning-based tracker. © 2018 IEEE.

关键词： Convolution

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Wavelet-based enhancement of color image using combined local variance and entropy analysis

Wavelet-based enhancement of color image using combined loca...

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2010 International Conference on Consumer Electronics, ICCE 2010

作者： Jun, Sinyoung Lee, Eunsung Kim, Sangjin Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424443161

This paper presents a novel wavelet-based image enhancement approach to reduce noise in a single-sensor color camera. Most traditional noise reduction methods tend to over-suppress high-frequency details. For overcoming this problem we first decompose the input image into flat and edge regions, and adaptively remove noise by multiplying alpha map and wavelet transform coefficients of LH, HL, and HH bands. After removing noise in the flat region, we further remove noise in edge regions by adaptive shrink of wavelet coefficients based on the entropy. Experimental result show the proposed algorithm is proved to be able to reduce noise without losing sharp details and is suitable for commercial low-cost such as digital cameras, CCTV, and surveillance systems. ©2010 IEEE.

关键词： image enhancement

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image restoration using alpha map-based fusion for multiple color aperture computational camera

Image restoration using alpha map-based fusion for multiple ...

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2010 International Conference on Consumer Electronics, ICCE 2010

作者： Lee, Eunsung Maik, Vivek Jun, Sinyoung Kim, Sangjin Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424443161

We present a novel image restoration approach by fusing multiple images acquired by a multiple color aperture computational camera for overcoming the limit of conventional image restoration algorithms, such as high computational load and post-processing artifacts. The proposed restoration framework utilizes color matching, alignment, and fusion of multiple images acquired by using multiple color apertures. The alpha map-based image fusion step successfully restores high-frequency components of meaningful edge regions. The proposed scheme can achieve automatic focusing and varying depth of filed at low cost for low-end consumer cameras. ©2010 IEEE.

关键词： Color

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Contrast enhancement for low-light image enhancement: A survey

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IEIE Transactions on Smart processing and Computing 2018年第1期7卷 36-48页

作者： Park, Seonhee Kim, Kiyeon Yu, Soohwan Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul06974 Korea Republic of Department of Integrative Engineering Chung-Ang University Seoul06974 Korea Republic of

In this paper, various contrast and low-light image enhancement methods are described and classified into three categories: I) histogram-based, ii) transmission map-based, and iii) retinexbased. The performance of the image enhancement algorithms is evaluated by comparing the resulting images using low-contrast and low-light images acquired under different illumination conditions. Various image enhancement algorithms are analyzed for both performance and efficiency. The described image enhancement algorithms can be applied to various visual surveillance or video analytics applications with low-light, low-contrast video input. © 2018 The Institute of Electronics and Information Engineers.

关键词： image enhancement

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Efficient Light-Weight Deep Neural Network for Person Detection in Drone images

Efficient Light-Weight Deep Neural Network for Person Detect...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Kim, Mingi Kim, Heegwang Mok, Yeongheon Paik, Joonki Image Processing and Intelligent System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea Republic of Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781665441544

In this paper, we propose an efficient light-weight deep neural network model for small object (person) detection in drone images. The proposed method performs light-weight as well as efficient small object detection by removing the head layers that detects large and medium-sized objects. In addition, the feature was extracted by focusing the weight on the small object while performing feature fusion through the Weighting Module. Finally, since the class imbalance problem between the object and the background is more serious in the drone image, the problem is alleviated by using the focal loss. As a result, the light-weight that can be mounted on the drone and the inference time are faster, and the Average Precision (AP) is higher than the original model. © 2022 IEEE.

关键词： Drones

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Flicker-free low-light video enhancement using patch-similarity and adaptive accumulation

Flicker-free low-light video enhancement using patch-similar...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Seungyong Ko Soohwan Yu Wonseok Kang Donggyun Kim Joonki Paik Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science

Recently, the various low-light enhancement methods have been proposed for consumer electronic devices. Although the existing methods provide the better brightness enhancement results, these methods generate the flicker effects during the video enhancement process. Flicker effects are caused by ignoring the correlation information between adjacent frames (i.e. inter-frames). To minimize the flicker effects, the proposed method uses the patch-similarity and adaptive accumulation in the inter-frames domain, and it makes the mean brightness values of enhanced video frames be preserved. The experimental results show that the proposed method can better improve the video frames without flicker effects than existing enhancement methods, and it can be applied to the image signal processing (ISP) chain for consumer electronic devices.

关键词： image color analysis Streaming media Consumer electronics Brightness Dynamic range Signal to noise ratio Distortion

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Learning-based Framework for Color Conversion between Digital Cinema Package and Streaming Movie

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IEIE Transactions on Smart processing and Computing 2022年第5期11卷 324-331页

作者： Lim, Younghoon Park, Kwanwoo Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul06975 Korea Republic of Samsung Research Seoul06765 Korea Republic of Graduate School of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of

Humans feel different experiences when viewing a cinema at the theater and home. This paper presents methods to reduce the difference in the viewing experience. Based on the workflow of digital cinema distribution, the proposed method attempts to minimize the color difference between digital cinema packages (DCP) for the theater and streaming movies. For end-to-end mapping between the DCP and streaming movie, this paper proposes a convolutional neural network (CNN)-based color conversion algorithm based on the SMPTE standard. The proposed method consists of three steps: i) color conversion using standard matrices, ii) color conversion using the CNN, and iii) color saturation error removal by fusing the results in steps i) and ii). The proposed method enhances the color of TV streaming images because it minimizes the color difference from the DCP and appropriately extends the color gamut. As a result, the proposed method can provide consumers with indistinguishable quality from a DCP movie at the theater. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Color

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Hybrid Auto-Focusing system Using Dual Pixel-Type CMOS SensorWith Contrast Detection Algorithm

Hybrid Auto-Focusing System Using Dual Pixel-Type CMOS Senso...

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imaging systems and Applications, IS 2016

作者： Jang, Jinbeum Park, Sangwoo Jo, Jieun Kim, Jongheon Paik, Joonki Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul156-756 Korea Republic of SK Hynix Gyeonggi-do463-844 Korea Republic of

This paper presents a hybrid auto-focusing system combining two passive methods. The proposed method measures a phase difference using dual pixel-type sensor with black masks, and detects the maximum contrast using mu...

ISBN: (纸本)9781943580156

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