The Plenoptic 2.0 Toolbox: Benchmarking of Depth Estimation Methods for MLA-Based Focused Plenoptic Cameras

Palmieri, Luca; Koch, Reinhard; Veld, Ron op het

doi:10.1109/icip.2018.8451073

Cited by 11 publications

(17 citation statements)

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“…During the actual process of calculating refocused images or total focus images, most methods [9,[13][14][15][16][17] use the center of micro-image to approximate the center of micro-lens. Essentially, this is equivalent to refocusing based on Equation (5) other than Equation (1).…”

Section: Comparison Of Refocusing Algorithmmentioning

confidence: 99%

“…Although it is easier to extract features from the total focus image, the traditional refocusing algorithm of focused plenoptic camera has some limitations. The traditional refocusing algorithm is based on the concept of virtual depth [2], which is widely used in the calculation of depth maps and total focus image [9,[13][14][15][16][17]. However, the theoretical imaging range of the refocused image based on traditional method [2] is limited by the sensor size and the traditional focal stack do not conform to the central projection in theory.…”

Section: Introductionmentioning

confidence: 99%

“…However, the theoretical imaging range of the refocused image based on traditional method [2] is limited by the sensor size and the traditional focal stack do not conform to the central projection in theory. Besides, most methods [9,[13][14][15][16][17] ignore the difference between the center of micro-lens and micro-image when calculating depth maps and total focus images, which will result in the deviation of calculated results from the theoretical model.…”

Section: Introductionmentioning

confidence: 99%

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Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

et al. 2020

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Fast and accurate feature extraction can lay a solid foundation for scene reconstruction and visual odometry. However, this has been rather a difficult problem for the focused plenoptic camera. In this paper, to the best of our knowledge, we first introduce an accurate and fast feature extraction algorithm based on central projection stereo focal stack (CPSFS). Specifically, we propose a refocusing algorithm that conforms to the central projection with regard to the center of main lens, which is more accurate than traditional one. On this basis, the feature is extracted on the CPSFS without calculating dense depth maps and total focus images. We verify the precision and efficiency of the proposed algorithm through simulated and real experiments, and give an example of scene reconstruction based on the proposed method. The experimental results show that our feature extraction algorithm is able to support the feature-based scene reconstruction via focused plenoptic camera.

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Section: Comparison Of Refocusing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

et al. 2020

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“…We acknowledge the lack of ground truth images in this field, therefore we propose a numerical evaluation via synthetic images. In recent works Blender has shown to be suitable for emulating light field behaviour and complex scenes [30,50]. We have chosen to use the Blender engine to simulate realistic images of the fibers as if they were captured using the FiMic, thus obtaining the respective ground truth data.…”

Section: Comparative Performance Analysismentioning

confidence: 99%

“…In previous works we extended similar applications to the case of focused plenoptic cameras (plenoptic 2.0) using MLA with a large number of microlenses, where the raw image contains micro-images capturing a portion of the scene. The main targets were the lens selection process for such MLA-based cameras [29] and evaluating different methods for depth estimation [30].…”

Section: Introductionmentioning

confidence: 99%

Robust Depth Estimation for Light Field Microscopy

Palmieri

Scrofani

Incardona

et al. 2019

Sensors

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Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization.

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