Simultaneous Estimation of Super-Resolved Image and 3D Information using Multiple Stereo-Pair Images

Kimura, K.; Nagai, T.; Nagayoshi, Hiroto; Suzuki, Hiroshi

doi:10.1109/icip.2007.4379854

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…In this section, two research challenges are discussed for future SR research: 1) multi-view SR imaging [108][109][110], and 2) temporal SR video (by increasing the frame rate) [111][112][113][114].…”

Section: Future Challengesmentioning

confidence: 99%

A survey on super-resolution imaging

Tian

2011

SIViP

260

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The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as 'low-resolution' images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of SR image and video reconstruction methods developed in the literature and highlight the future research challenges. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images, and the SR video approaches reconstruct an image sequence with a higher-resolution from a group of adjacent lower-resolution image frames. Furthermore, several SR applications are discussed to contribute some insightful comments on future SR research directions. Specifically, the SR computations for multi-view images and the SR video computation in the temporal domain are discussed.

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Section: Future Challengesmentioning

confidence: 99%

A survey on super-resolution imaging

Tian

2011

SIViP

260

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“…Therefore, these small motions are first found in the smaller images and then are interpolated in the lower level (higher resolution) images of the pyramid until the original image is met. This method, known as optical flow, works quite well when motion is to be computed between objects, which are non-rigid, non-planar, non-Lambertian, and are subject to self occlusion, like human faces, [55], [57], [58], [71], [82], [99], [100], [115], [116], [128], [126], [176], [190], [270], [288], [295], [296], [299], [307], [414], [468], [478], [492], [500], [529], [534], [555], [592]. It is discussed in [592] that using optical flows of strong candidate feature points (like those obtained by Scale Invariant Feature Transform (SIFT)) for SR algorithms produces better results than dense optical flows in which the flow involves every pixel.…”

Section: Geometric Registrationmentioning

confidence: 99%

“…It is shown in [274] that this method works better than registration algorithms based on Harris corners. - [296] normalized cross correlation has been used to obtain the disparity for registration in a stereo setup for 3D SR.…”

Section: Geometric Registrationmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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“…In this research, each pixel in multiple images is corresponded with high accuracy without the strong constraints for a target scene such as the planar assumption by a one-dimensional search of depth under the condition that intrinsic and extrinsic camera parameters are known. In work similar to our method, the spatial super-resolution method that uses a depth map has already been proposed [12]. However, this method needs stereopair images and does not increase the temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Super-Resolution Using Depth Map

et al. 2009

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Abstract. This paper describes a spatio-temporal super-resolution method using depth maps for static scenes. In the proposed method, the depth maps are used as the parameters to determine the corresponding pixels in multiple input images by assuming that intrinsic and extrinsic camera parameters are known. Because the proposed method can determine the corresponding pixels in multiple images by a one-dimensional search for the depth values without the planar assumption that is often used in the literature, spatial resolution can be increased even for complex scenes. In addition, since we can use multiple frames, temporal resolution can be increased even when large parts of the image are occluded in the adjacent frame. In experiments, the validity of the proposed method is demonstrated by generating spatio-temporal super-resolution images for both synthetic and real movies.

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Simultaneous Estimation of Super-Resolved Image and 3D Information using Multiple Stereo-Pair Images

Cited by 7 publications

References 4 publications

A survey on super-resolution imaging

A survey on super-resolution imaging

Super-resolution: a comprehensive survey

Spatio-temporal Super-Resolution Using Depth Map

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