High-speed all-optical Haar wavelet transform for real-time image compression

Alemohammad, Milad; Stroud, Jasper R.; Bosworth, Bryan T.; Foster, Mark A.

doi:10.1364/oe.25.009802

Cited by 35 publications

(13 citation statements)

References 25 publications

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“…The Haar wavelet was proposed by Alfréd Haar in 1909 [ 27 ]. It is the earliest proposed and simplest type of wavelet transforms [ 32 ]. With two pixels ( p 1 and p 2 ) in the image, the Haar wavelet can be implemented to the low-band and high-band by

and

respectively.…”

Section: Related Methodsmentioning

confidence: 99%

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Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection

Liaw

Huang

et al. 2020

Sensors

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One of the common methods for measuring distance is to use a camera and image processing algorithm, such as an eye and brain. Mechanical stereo vision uses two cameras to shoot the same object and analyzes the disparity of the stereo vision. One of the most robust methods to calculate disparity is the well-known census transform, which has the problem of conversion window selection. In this paper, three methods are proposed to improve the performance of the census transform. The first one uses a low-pass band of the wavelet to reduce the computation loading and a high-pass band of the wavelet to modify the disparity. The main idea of the second method is the adaptive size selection of the conversion window by edge information. The third proposed method is to apply the adaptive window size to the previous sparse census transform. In the experiments, two indexes, percentage of bad matching pixels (PoBMP) and root mean squared (RMS), are used to evaluate the performance with the known ground truth data. According to the results, the computation required can be reduced by the multiresolution feature of the wavelet transform. The accuracy is also improved with the modified disparity processing. Compared with previous methods, the number of operation points is reduced by the proposed adaptive window size method.

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and

respectively.…”

Section: Related Methodsmentioning

confidence: 99%

“…With two pixels ( p 1 and p 2 ) in the image, the Haar wavelet can be implemented to the low-band and high-band by

and

respectively. In practice, the Haar wavelet can be described as a transformation matrix [ 32 ]:

…”

Section: Related Methodsmentioning

confidence: 99%

Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection

Liaw

Huang

et al. 2020

Sensors

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show abstract

“…Besides using SLM, structured illumination can be performed by using electro-optic modulation of broadband laser sources to conduct basis scan and compressive imaging [26][27][28]. Fast switching LED micro-arrays have been used 3D phase and intensity microscopy, super-resolution microscopy, and neural stimulation [29,30], and the concept of using LED arrays in the context of computational ghost imaging was recently considered theoretically in [31].…”

Section: Illumination Modulementioning

confidence: 99%

1000 fps computational ghost imaging using LED-based structured illumination

Xu¹,

Chen²,

Penuelas³

et al. 2018

Opt. Express

204

103

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Single-pixel imaging uses a single-pixel detector, rather than a focal plane detector array, to image a scene. It provides advantages for applications such as multi-wavelength, three-dimensional imaging. However, low frame rates have been a major obstacle inhibiting the use of computational ghost imaging technique in wider applications since its invention one decade ago. To address this problem, a computational ghost imaging scheme, which utilizes an LED-based, high-speed illumination module is presented in this work. At 32 × 32 pixel resolution, the proof-of-principle system achieved continuous imaging with 1000 fps frame rate, approximately two orders larger than those of other existing ghost imaging systems. The proposed scheme provides a cost-effective and high-speed imaging technique for dynamic imaging applications.

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“…• Finally, the response value in HD, absolute value in HD, response value in VD, and absolute value in VD of the Haar wavelet [22] transform are used to form the 64D description vectors, as shown in Figure 9.…”

Section: Obtaining the Description Vector Of Fpsmentioning

confidence: 99%

Docking navigation method for UAV autonomous aerial refueling

Luo

Shao

et al. 2018

Sci. China Inf. Sci.

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In this paper, a docking navigation method for autonomous aerial refueling (AAR) of unmanned aerial vehicles (UAVs) based on a binocular vision system (BVS) is proposed. A BVS simulation platform is built for simulation research purposes. First, unnecessary scene information in the image is filtered through green light-emitting diodes (LEDs) and filters. Then the image is processed via graying, binarization, and median filtering to highlight the connected area of the LED in the image. Subsequently, the center of mass of the connected area is selected as the feature point (FP), and the FPs are described using an improved Haar wavelet transform. The multidimensional description vector of FP is obtained and matched. Finally, the position and pose of the refueling cone sleeve are estimated. Simulation results show the effectiveness of the presented AAR navigation method.

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High-speed all-optical Haar wavelet transform for real-time image compression

Cited by 35 publications

References 25 publications

Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection

Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection

1000 fps computational ghost imaging using LED-based structured illumination

Docking navigation method for UAV autonomous aerial refueling

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