• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.149-154
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• 2023

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

• 한국광학회:학술대회논문집
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• 한국광학회 2008년도 하계학술발표회 논문집
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• pp.119-120
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• 2008

A HMD type multi-focus 3D display system is developed and proof about satisfaction of eye accommodation is tested. Four LEDs(Light Emitting Diode) and a DMD are used to generate four parallax images at single eye and any mechanical part is not included in this system. The multi-focus means the ability of monocular depth cue to various depth levels. By achieving multi-focus function, we developed a 3D display system for only one eye, which can satisfy the accommodation to displayed virtual objects within defined depth. We could achieve a result that focus adjustment is possible at 5 step depths in sequence within 2m depth for only one eye. Additionally, the change level of burring depending on the focusing depth is tested by captured photos and moving pictures of video camera and several subjects. And the HMD type multi-focus 3D display can be applied to a monocular 3D display and monocular AR 3D display.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.39-47
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• 2023

This study compares various transformation techniques for multifocus image fusion. Multi-focus image fusion is a procedure of merging multiple images captured at unalike focus distances to produce a single composite image with improved sharpness and clarity. In this research, the purpose is to compare different popular frequency domain approaches for multi-focus image fusion, such as Discrete Wavelet Transforms (DWT), Stationary Wavelet Transforms (SWT), DCT-based Laplacian Pyramid (DCT-LP), Discrete Cosine Harmonic Wavelet Transform (DC-HWT), and Dual-Tree Complex Wavelet Transform (DT-CWT). The objective is to increase the understanding of these transformation techniques and how they can be utilized in conjunction with one another. The analysis will evaluate the 10 most crucial parameters and highlight the unique features of each method. The results will help determine which transformation technique is the best for multi-focus image fusion applications. Based on the visual and statistical analysis, it is suggested that the DCT-LP is the most appropriate technique, but the results also provide valuable insights into choosing the right approach.

• 한국통신학회논문지
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• 제34권10B호
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• pp.1117-1123
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• 2009

DMD(Digital Micro-mirror Device)와 갈바노 스캐너를 이용한 두부 고정형 다초점 디스플레이 시스템은 초점조절이 가능하여 스테레오스코픽 디스플레이에서 발생하는 양안의 수렴작용과 단안의 초점조절 작용의 불일치에 의한 눈의 피로 문제를 해결할 수 있는 디스플레이 시스템이다. 이 시스템은 레이저 스캐닝 방법으로 각 시점에 대한 영상의 수렴점을 형성하여 관찰자 동공에 제공함으로 초점조절이 가능토록 한다. 본 논문에서는 이 다초점 디스플레이 시스템의 레이저 스캐닝 방법을 수학적으로 분석하였고, 이러한 분석에 기반 한 다초점 시스템을 제작하여 비디오카메라를 통하여 초점조절이 가능함을 보였고, 결과적으로 레이저 스캐닝 방법에 의한 다초점 시스템의 시점 형성원리가 검증되었다.

• Journal of applied mathematics & informatics
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• 제41권5호
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• pp.1115-1128
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• 2023

In this paper, a novel hybrid method for multi-focus image fusion is proposed. The method combines the advantages of wavelet transform-based methods and focus-measure-based methods to achieve an improved fusion result. The input images are first decomposed into different frequency sub-bands using the discrete wavelet transform (DWT). The focus measure of each sub-band is then calculated using the Laplacian of Gaussian (LoG) operator, and the sub-band with the highest focus measure is selected as the focused sub-band. The focused sub-band is sharpened using an unsharp masking filter to preserve the details in the focused part of the image.Finally, the sharpened focused sub-bands from all input images are fused using the maximum intensity fusion method to preserve the important information from all focus images. The proposed method has been evaluated using standard multi focus image fusion datasets and has shown promising results compared to existing methods.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2006년도 학술대회 1부
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• pp.1081-1086
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• 2006

일반적으로 프로젝터는 심도(depth of field)가 제한되어 있기 때문에 스크린이 곡면일 경우, 일부 영역에서는 초점이 맞지 않게(out-of-focus) 된다. 이런 out-of-focus 영역의 정보는 블러링(blurring)되기 때문에 사용자에게 정확한 정보를 전달할 수 없다. 여러 대의 프로젝터를 이용할 경우, 각 프로젝터는 다른 in-focus 영역을 가지기 때문에 각 프로젝터 픽셀의 in-focus/out-of-focus 판별을 통해 in-focus 픽셀만을 투사함으로써, out-of-focus 픽셀의 영향을 제거할 수 있다. 그러나 여러 대의 프로젝터의 in-focus영역이 거의 일치할 경우, out-of-focus영역은 여전히 out-of-focus상태일 수 밖에 없다. 따라서, 각 프로젝터의 초점을 유연하게 조절하면서 동시에 여러 대의 프로젝터에 의한 in-focus 영역을 최대한 크게 할 수 있는 방법이 필요하다. 본 논문에서는 각 프로젝터의 초점을 유동적으로 조절하면서 취득된 영상을 처리하여 in-focus 영역을 판별하고 각 프로젝터에 대한 in-focus 영역을 조합하여 전체in-focus 영역의 면적을 최대화하는 방법을 제안한다. 제안된 방법의 유용성을 검증하기 위해, 각 프로젝터의 in-focus 영역을 적절한 컬러를 이용해 시각적으로 표현해주고 이 정보를 참조해 각 프로젝터의 초점을 유동적으로 조절하여 전초점 영상을 만들어내는 시스템을 구현하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1234-1257
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• 2023

In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.

• 방송공학회논문지
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• 제11권4호
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• pp.441-447
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• 2006

투시형 두부 장착형 디스플레이(HMD) 방식의 3차원 디스플레이는 스테레오스코픽 디스플레이에 의한 가상의 3차원 데이터와 실제 장면을 동시에 볼 수 있는 장점(MR-Mixed Reality)이 있다. 그러나 장시간 사용 시 눈의 피로현상이 발생한다. 또한 가상 데이터의 고정된 초점으로 인한 비초점화 현상이 발생한다. 이것의 중요한 요인 중 하나가 눈의 초점조절 능력 불만족이다. 이것의 해결방법으로 투시형 HMD에 초점조절 가능 3차원 디스플레이(다초점 3차원 디스플레이) 기술의 적용을 제안하였다. 결과적으로, 단안에서 현실세계의 물체에 대한 초점조절과 다초점에 의한 가상데이터의 초점조절이 일치함을 확인하였다.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1293-1300
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• 2018

For the disadvantages of multi-scale geometric analysis methods such as loss of definition and complex selection of rules in image fusion, an improved multi-focus image fusion method is proposed. First, the initial fused image is quickly obtained based on the lifting stationary wavelet transform, and a simple normalized cut is performed on the initial fused image to obtain different segmented regions. Then, the original image is subjected to NSCT transformation and the absolute value of the high frequency component coefficient in each segmented region is calculated. At last, the region with the largest absolute value is selected as the postfusion region, and the fused multi-focus image is obtained by traversing each segment region. Numerical experiments show that the proposed algorithm can not only simplify the selection of fusion rules, but also overcome loss of definition and has validity.