A dedicated hardware architecture for real-time auto-focusing using an FPGA

Jin, Seunghun; Cho, Junguk; Kwon, Key Ho; Jeon, Jae Wook

doi:10.1007/s00138-009-0190-2

Cited by 8 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the contrast computation is a little more complex than CMSL, the computation time is acceptable and will not cause any lag in our passive AF system. In addition, the dedicated hardware architecture for real-time auto-focusing, such as field programmable gate array (FPGA)-based AF system [ 34 ], can be used to shorten the computation time, which processes the incoming pixels simultaneously with their neighboring pixels based on its parallelized window processing architecture. The extra computation in CMAN aims to distinguish the best focused peak from noise influence which is critical to ensure the focusing accuracy and reliability.…”

Section: Proposed Passive Af Algorithmmentioning

confidence: 99%

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Wang

Tang

et al. 2011

Sensors

View full text Add to dashboard Cite

Low contrast images, suffering from a lack of sharpness, are easily influenced by noise. As a result, many local false peaks may be generated in contrast measurements, making it difficult for the camera’s passive auto-focus system to perform its function of locating the focused peak. In this paper, a new passive auto-focus algorithm is proposed to address this problem. First, a noise reduction preprocessing is introduced to make our algorithm robust to both additive noise and multiplicative noise. Then, a new contrast measure is presented to bring in local false peaks, ensuring the presence of a well defined focused peak. In order to gauge the performance of our algorithm, a modified peak search algorithm is used in the experiments. The experimental results from an actual digital camera validate the effectiveness of our proposed algorithm.

show abstract

Section: Proposed Passive Af Algorithmmentioning

confidence: 99%

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Wang

Tang

et al. 2011

Sensors

View full text Add to dashboard Cite

show abstract

“…LT on a parallel computer (Mitrion Virtual Processor) should run in a pipeline manner and take advantage of FPGA parallelism. There are two known FPGA implementations of local thresholding algorithms: by Jin et al (2009) and Gorgon and Tadeusiewicz (2000). In the first one a simplified version of the LT algorithm, which is a part of an passive auto-focusing system, was implemented in the FPGA.…”

Section: Implementing Local Thresholding In Mitrion-cmentioning

confidence: 99%

Parallel implementation of local thresholding in Mitrion-C

Kryjak¹,

Gorgon²

2010

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

show abstract

“…Jin devised a hardware accelerator linking a camera with a framegrabber. The hardware accelerator was constructed using a field-programmable gate-array (FPGA) which lowered the computational load on the PC [8] but the hardware cost was relatively high because of various connection interfaces. Thus, the hardware approach is inconvenient for supporting the diverse formulations of DFIs.…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors

2023

KSII TIIS

View full text Add to dashboard Cite

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

show abstract

A dedicated hardware architecture for real-time auto-focusing using an FPGA

Cited by 8 publications

References 7 publications

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Parallel implementation of local thresholding in Mitrion-C

Parallel Implementations of Digital Focus Indices Based on Minimax Search Using Multi-Core Processors

Contact Info

Product

Resources

About