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Moon, Byeongho,Yu, Soohwan,Ko, Seungyong,Park, Seonhee,Paik, Joonki Optical Society of America 2017 Journal of the Optical Society of America A Vol.34 No.6
<P>This paper presents a digital zooming method using a super-resolution (SR) algorithm based on the local self-similarity between the wide-and tele-view images acquired by an asymmetric dual camera system. The proposed SR algorithm consists of four steps: (i) registration of an optically zoomed image to the wide-view image, (ii) restoration of the central region of the zoomed wide-view image, (iii) restoration of the boundary region of the zoomed wideview image, and (iv) fusion of the results from steps (ii) and (iii). Since an asymmetric dual camera system acquires different-resolution images on the same scene due to the different optical specifications, the proposed method can restore the low-resolution wide-view image using the ideal high-frequency component estimated from the optically zoomed image. Experimental results demonstrate that the proposed method can provide significantly improved high-resolution wide-view images compared to existing single-image- based SR methods. (C) 2017 Optical Society of America</P>
Low-light image enhancement using variational optimization-based retinex model
Seonhee Park,Soohwan Yu,Byeongho Moon,Seungyong Ko,Joonki Paik IEEE 2017 IEEE transactions on consumer electronics Vol.63 No.2
<P>This paper presents an optimization-based low-light image enhancement method using spatially adaptive l(2)-norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the l(2)-norm minimization artifacts at the low computational cost.</P>
An Optimal Low Dynamic Range Image Generation Method Using a Neural Network
Park, Kwanwoo,Yu, Soohwan,Park, Seonhee,Lee, Sangkeun,Paik, Joonki IEEE 2018 IEEE transactions on consumer electronics Vol.64 No.1
<P>This paper presents a neural network-based method to generate multiple images with different exposures from a single input low dynamic range (LDR) image for improved high dynamic range (HDR) imaging. The proposed algorithm consists of three steps: 1) 2-D histogram estimation; 2) neural network-based LDR images estimation; and 3) generation of an optimal set of differently exposed images. The proposed method first generates image features by estimating a patched-based 2-D histogram. The extracted features are used in an input layer of the neural network, which plays a role to select an optimal set of LDR images. A set of LDR images is generated using a curvature-based contrast enhancement method. Experimental results show that the proposed method can generate an optimal set of LDR images using neural network and provide improved HDR images. In addition, the proposed method can be implemented as a preprocessing step in most existing HDR frameworks. The proposed HDR approach is considered as a single-input method that gives almost the same performance to multiple image-based HDR method.</P>
Artifact-Free Low-Light Video Enhancement Using Temporal Similarity and Guide Map
Ko, Seungyong,Yu, Soohwan,Kang, Wonseok,Park, Chanyong,Lee, Sangkeun,Paik, Joonki Institute of Electrical and Electronics Engineers 2017 IEEE transactions on industrial electronics Vol.64 No.8
<P>This paper presents a low-light video restoration algorithm using similar patches among temporally adjacent frames. The proposed artifact-free low-light video restoration algorithm consists of three steps: 1) brightness enhancement using similar patches among temporally adjacent frames and adaptive accumulation; 2) improved color assignment to reduce color distortion; and 3) image fusion for saturation reduction using the guide map. The proposed brightness enhancement step guarantees not to produce any undesired artifacts because of searching the most similar patches among given set of temporally adjacent frames. The color assignment and fusion steps enable a fully automatic color preservation and average brightness control. Experimental results show that the proposed algorithm can better restore high-quality videos without undesired artifacts such as noise amplification, flicker, color distortion, and brightness saturation. As a result, the proposed algorithm can be implemented in a wide range of digital imaging applications such as video surveillance systems and advanced driver assistance systems.</P>