Focus measurement in 3D focal stack using direct and inverse discrete radon transform

Gómez-Cárdenes, Óscar; Marichal-Hernández, José G.; Trujillo-Sevilla, Juan M.; Carmona-Ballester, David; Rodríguez-Ramos, José Manuel

doi:10.1117/12.2261921

Cited by 3 publications

(2 citation statements)

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“…This gives rise to the possibility of creating a new detector that is valid for two-dimensional codes and still reuse the decoder. Another future line of work could be to use the decoder in other problems for which an appropriate encoder can be rewritten, such as the depth-from-focus problem [55,56].…”

Section: Future Lines Of Researchmentioning

confidence: 99%

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

Gómez-Cárdenes

Marichal-Hernández

Son

et al. 2023

Sensors

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In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder–decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method’s processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.

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Section: Future Lines Of Researchmentioning

confidence: 99%

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

Gómez-Cárdenes

Marichal-Hernández

Son

et al. 2023

Sensors

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“…An invertible, Curvelet transform, running exclusively in integer arithmetics, was introduced in [38]. Inside Curvelet transform, conventional DRT was used at the Radon level.…”

Section: Example Of Periodic Drt Applicationmentioning

confidence: 99%

Central and Periodic Multi-Scale Discrete Radon Transforms

et al. 2021

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The multi-scale discrete Radon transform (DRT) calculates, with linearithmic complexity, the summation of pixels, through a set of discrete lines, covering all possible slopes and intercepts in an image, exclusively with integer arithmetic operations. An inversion algorithm exists and is exact and fast, in spite of being iterative. In this work, the DRT forward and backward pair is evolved to propose two faster algorithms: central DRT, which computes only the central portion of intercepts; and periodic DRT, which computes the line integrals on the periodic extension of the input. Both have an output of size N×4N, instead of 3N×4N, as in the original algorithm. Periodic DRT is proven to have a fast inversion, whereas central DRT does not. An interesting application of periodic DRT is its use as building a block of discrete curvelet transform. Central DRT can provide almost a 2× speedup over conventional DRT, probably becoming the faster Radon transform algorithm available, at the cost of ignoring 15% of the summations in the corners.

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