All-in-Focus Image Generation by Merging Multiple Differently Focused Images in Three-Dimensional Frequency Domain

Mo, Hiroshi; Kubota, Akira

doi:10.1007/11581772_27

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…By applying a stable two-dimensional filter to the integrated image, an all-in-focus image can be robustly reconstructed much faster than the previously proposed three-dimensional filtering [9].…”

Section: Resultsmentioning

confidence: 98%

“…Previously, we proposed a method of all-in-focus image reconstruction by applying a three-dimensional filter to the multi-focus imaging sequences [9]. However, the cost of such a three-dimensional filtering is not inexpensive, if the number of multiple differently focused images or the image size increases.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are not easy to extend for merging very many images, for example, to restore textures of complex scenes. Therefore, we deal with a method of integrating the multiple differently focused images into desired images directly [9,10].…”

Section: Introductionmentioning

confidence: 99%

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High-Speed All-in-Focus Image Reconstruction by Merging Multiple Differently Focused Images

Kubota

2006

Advances in Multimedia Information Processing - PCM 2006

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Abstract. This paper deals with high-speed all-in-focus image reconstruction by merging multiple differently focused images. Previously, we proposed a method of generating an all-in-focus image from multifocus imaging sequences based on spatial frequency analysis using threedimensional FFT. In this paper, first, we combine the sequence into a two-dimensional image having fine quantization step size. Then, just by applying a certain convolution using two-dimensional FFT to the image, we realize high-speed reconstruction of all-in-focus images robustly. Some simulations utilizing synthetic images are shown and conditions achieving the good quality of reconstructed images are discussed. We also show experimental results of high-speed all-in-focus image reconstruction compared with those of the previous method by using real images.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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High-Speed All-in-Focus Image Reconstruction by Merging Multiple Differently Focused Images

Kubota

2006

Advances in Multimedia Information Processing - PCM 2006

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“…The proposed FA model uses parallax cues instead of defocus cues and requires only color filters as additional optical elements to the lens without requiring multiple exposures. Digital image processing methods for auto-focusing: Extensive work has been done using fusion and restoration-based methods for removal of out-of-focus blur in images [4][5][6][7]. Fusion algorithms using DCT, pyramids, and wavelets have been proposed to name a few where as restoration algorithms include blind deconvolution with no priori information as well as with PSF estimation.…”

Section: Related Workmentioning

confidence: 99%

Computational filter-aperture approach for single-view multi-focusing

Maik

Cho

Kim

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Most of the focusing techniques need to estimate depth information for ensuring that the object of interest is at an appropriate distance for full frontal focus. Computational cameras which can variably focus different regions of the scene with large depth of field have been proposed. In this paper we propose a full auto-focusing algorithm using computational camera without involving any digital image restoration methods and just one input. The proposed computational camera uses multiple filter apertures corresponding to each color channel which can acquire three shifted views of a scene in the RGB color planes. We can make any region focused by appropriately shifting each color channel to be aligned. Depth map estimation is carried out to extract different regions from these channel shifted images which is later fused to produce the final image without any focal blur. Experimental results show performance and feasibility of the proposed algorithm for autofocusing images with one or more differently out-of-focused objects.

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“…A noise-robust selective fusion algorithm has been proposed for computing the allin-focus image from a sequence of images with limited depth of field [16]. Merging multiple differently focused images in 3-D frequency domain is an important and highly effective method [17]. A 2-D filter from acquired multi-focus imaging sequences has also been used for reconstructing all-in-focus images [18].…”

Section: Introductionmentioning

confidence: 99%

Depth-based computational photography

Liu

et al. 2015

Journal of Modern Optics

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A depth-based computational photography model is proposed for all-in-focus image capture. A decomposition function, a defocus matrix, and a depth matrix are introduced to construct the photography model. The original image acquired from a camera can be decomposed into several sub-images on the basis of depth information. The defocus matrix can be deduced from the depth matrix according to the sensor defocus geometry for a thin lens model. And the depth matrix is reconstructed using the axial binocular stereo vision algorithm. This photography model adopts an energy functional minimization method to acquire the sharpest image pieces separately. The implementation of the photography method is described in detail. Experimental results for an actual scene demonstrate that our model is effective. IntroductionThe development of photography has involved four main stages: pinhole photography, lens photography, digital photography, and computational photography [1]. Computational photography contributes to recording of a more plentiful visual experience, capturing information beyond just a simple set of pixels and making the recorded scene representation far more machine readable [2]. The most important goal of computational photography is to extend the depth of field and the field of view. The representative research interests of computational photography include areas such as the design of special optics, improvement of digital sensors, application of modern processors, development of light-field photography, and implementation of three-dimensional (3-D) imaging [3][4][5][6][7]. In the case of 3-D imaging, a multi-view imaging system with axially distributed stereo image sensing and ray back-projection has been proposed for object visualization of partially occluded [8]. A unifying framework has been presented to evaluate the lateral and axial resolution of N-ocular imaging systems under fixed resource constraints [9]. Apart from these studies, reconstructions of defocus and depth maps, which can be achieved using the defocus-from-depth method [10], are important issues in 3-D photography. This approach also describes the relationships among image defocus, sensor defocus, and scene defocus in a camera system.Here, we present a depth-based computational photography model, which is essentially a 3-D photography method, for extending the depth of field and reconstructing

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All-in-Focus Image Generation by Merging Multiple Differently Focused Images in Three-Dimensional Frequency Domain

Cited by 15 publications

References 12 publications

High-Speed All-in-Focus Image Reconstruction by Merging Multiple Differently Focused Images

High-Speed All-in-Focus Image Reconstruction by Merging Multiple Differently Focused Images

Computational filter-aperture approach for single-view multi-focusing

Depth-based computational photography

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