Plenoptic camera can sample object in the way of 4D light fields in a single snapshot, which is different from conventional camera. Vignetting in the plenoptic camera will result in non-uniform intensity distribution of 4D light field data. We propose a method in this paper to address this problem according to exposure fusion and patch-match. The uniform intensity distribution of 4D light field data can be enhanced by our approach. This work can be treated as a preprocess step for further application on 4D light field data. As more details are kept, the quality of depth estimation and digital refocusing with 4D light field data is increased by our method compared with previous method.
Counting the number of people is still an important task in social security applications, and a few methods based on video surveillance have been proposed in recent years. In this paper, we design a novel optical sensing system to directly acquire the depth map of the scene from one light-field camera. The light-field sensing system can count the number of people crossing the passageway, and record the direction and intensity of rays at a snapshot without any assistant light devices. Depth maps are extracted from the raw light-ray sensing data. Our smart sensing system is equipped with a passive imaging sensor, which is able to naturally discern the depth difference between the head and shoulders for each person. Then a human model is built. Through detecting the human model from light-field images, the number of people passing the scene can be counted rapidly. We verify the feasibility of the sensing system as well as the accuracy by capturing real-world scenes passing single and multiple people under natural illumination.
This paper proposes an approach to produce the super-resolution all-refocused images with the plenoptic camera. The plenoptic camera can be produced by putting a micro-lens array between the lens and the sensor in a conventional camera. This kind of camera captures both the angular and spatial information of the scene in one single shot. A sequence of digital refocused images, which are refocused at different depth, can be produced after processing the 4D light field captured by the plenoptic camera. The number of the pixels in the refocused image is the same as that of the micro-lens in the micro-lens array. Limited number of the micro-lens will result in poor low resolution refocused images. Therefore, not enough details will exist in these images. Such lost details, which are often high frequency information, are important for the in-focus part in the refocused image. We decide to super-resolve these in-focus parts. The result of image segmentation method based on random walks, which works on the depth map produced from the 4D light field data, is used to separate the foreground and background in the refocused image. And focusing evaluation function is employed to determine which refocused image owns the clearest foreground part and which one owns the clearest background part. Subsequently, we employ single image super-resolution method based on sparse signal representation to process the focusing parts in these selected refocused images. Eventually, we can obtain the super-resolved all-focus image through merging the focusing background part and the focusing foreground part in the way of digital signal processing. And more spatial details will be kept in these output images. Our method will enhance the resolution of the refocused image, and just the refocused images owning the clearest foreground and background need to be super-resolved.
Light field imaging is capable of capturing dense multi-view 2D images in one snapshot, which record both intensity values and directions of rays simultaneously. As an emerging 3D device, the light field camera has been widely used in digital refocusing, depth estimation, stereoscopic display, etc. Traditional multi-view stereo (MVS) methods only perform well on strongly texture surfaces, but the depth map contains numerous holes and large ambiguities on textureless or low-textured regions. In this paper, we exploit the light field imaging technology on 3D face modeling in computer vision. Based on a 3D morphable model, we estimate the pose parameters from facial feature points. Then the depth map is estimated through the epipolar plane images (EPIs) method. At last, the high quality 3D face model is exactly recovered via the fusing strategy. We evaluate the effectiveness and robustness on face images captured by a light field camera with different poses.
KEYWORDS: High dynamic range imaging, Cameras, Imaging systems, Coded apertures, Sensors, Image processing, Optical simulations, Coded aperture imaging, Photography, Signal to noise ratio
We present a high dynamic range (HDR) imaging system design scheme based on coded aperture technique. This
scheme can help us obtain HDR images which have extended depth of field. We adopt Sparse coding algorithm to
design coded patterns. Then we utilize the sensor unit to acquire coded images under different exposure settings. With
the guide of the multiple exposure parameters, a series of low dynamic range (LDR) coded images are reconstructed. We
use some existing algorithms to fuse and display a HDR image by those LDR images. We build an optical simulation
model and get some simulation images to verify the novel system.
This paper presents a new color image analysis approach which fuses several processing maps by Graph-Cuts algorithm
for Markov Random Field (MRF) in different color spaces. Recently, graph-based image analysis methods, such as
Graph-Cuts, have been achieved exciting results for approximate inference in Markov Random Field. One color image
can be represented in many color spaces, such as RGB, LAB and HSV, but existing Graph-Cuts approaches often
compute global energy function only in one color spaces and ignore that each color space has an interesting property for
certain applications respectively. This paper processes images in MRF and represents them in one MRF model firstly.
Then Graph-Cuts algorithms are used to process images in each color space and generate one map. Several processing
maps can be acquired from some color spaces. These maps are fused to get more reliable and accurate results. We select
stereo matching which can get depth maps from multi-view images to evaluate our image analysis approach. The
experiments herein reported in this paper illustrate the potential of this approach compared to existing Graph-Cuts
methods from processing results.
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