High dynamic range in videodensitometry—a comparative study to classic videoscanning on Gentiana extracts

Gadowski, Sebastian; Tomiczak, Karolina; Komsta, Łukasz

doi:10.1007/s00764-023-00226-3

“…CPU, GPU, and other processors that support OpenCL all belong to OpenCL devices. An OpenCL device can be divided into one or more compute units (CU), and each CU is composed of one or more processing elements (PEs) [32].…”

Section: Plos Onementioning

confidence: 99%

A parallel Canny edge detection algorithm based on OpenCL acceleration

Song,

Li,

Xiao

et al. 2024

PLoS ONE

4

0

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In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.

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“…OpenCL has a relatively wide range of applications, providing computing support for CPU, GPU, FPGA, and other devices, and has become a programming standard in the field of heterogeneous systems. OpenCL provides developers with a common programming interface and a development model for the underlying hardware layout [30]. An OpenCL device can be divided into one or more Compute Units (CU), and each CU is composed of one or more Processing Elements (PE) [31].…”

Section: Overview Of Openclmentioning

confidence: 99%

A parallel Canny edge detection algorithm based on OpenCL acceleration

Song

¹

,

Li

²

,

Zhou

³

et al. 2023

Preprint

0

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In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm can achieve 20.68 times, 3.96 times, and 1.21 times speedup ratio compared with CPU serial algorithm, CPU multi-threaded parallel algorithm, and CUDA-based parallel algorithm, respectively. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.

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“…For each combination, we used the novel acquisition approach developed in our laboratory and previously described [26]. Nine exposures in one exposure value step (EV) were taken and converted to a high-density range (HDR) image with 1280 × 720 pixel resolution.…”

Section: Image Acquisition Proceduresmentioning

confidence: 99%

“…It is used in artistic photography and the film industry [25]. Our recent research proved that it can also be used successfully in chromatographic video scanning [26]. The idea behind our approach was analogous to other applications of HDR photography: to take several shots of one plate with increasing exposures, then combine them into one image dataset with a huge range of dynamics.…”

Section: Introductionmentioning

confidence: 99%

Novel Hyperspectral Analysis of Thin-Layer Chromatographic Plates—An Application to Fingerprinting of 70 Polish Grasses

Wróbel-Szkolak

¹

,

Cwener

²

,

Komsta

³

2023

Molecules

Self Cite

0

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The advantages of hyperspectral imaging in videodensitometry are presented and discussed with the example of extracts from 70 Polish grasses. An inexpensive microscope camera was modified to cover the infrared spectrum range, and then 11 combinations of illumination (254 nm, 366 nm, white light), together with various filters (no filter, IRCut, UV, cobalt glass, IR pass), were used to register RGB HDR images of the same plate. It was revealed that the resulting 33 channels of information could be compressed into 5–6 principal components and then visualized separately as grayscale images. We also propose a new approach called principal component artificial coloring of images (PCACI). It allows easy classification of chromatographic spots by presenting three PC components as RGB channels, providing vivid spots with artificial colors and visualizing six principal components on two color images. The infrared region brings additional information to the registered data, orthogonal to the other channels and not redundant with photos in the visible region. This is the first published attempt to use a hyperspectral camera in TLC and it can be clearly concluded that such an approach deserves routine use and further attention.

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High dynamic range in videodensitometry—a comparative study to classic videoscanning on Gentiana extracts

Cited by 4 publications

References 31 publications

A parallel Canny edge detection algorithm based on OpenCL acceleration

A parallel Canny edge detection algorithm based on OpenCL acceleration

A parallel Canny edge detection algorithm based on OpenCL acceleration

Novel Hyperspectral Analysis of Thin-Layer Chromatographic Plates—An Application to Fingerprinting of 70 Polish Grasses

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