FPGA implementation of the coupled filtering method

Zhang, Chen; Liang, Tianzhu; Mok, Philip K. T.; Yu, Weichuan

doi:10.1109/bibm.2016.7822556

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…In recent years, in order to meet the application of disaster relief, military deployment and other situations, the demand for fast and real-time remote sensing image orthographic correction is increasing. To improve the image processing speed, researchers have proposed various parallel processing and hardware acceleration methods [7][8][9][10][11]. For example, Warpenburg and Siegel perform resampling in the single-instruction multi-data stream environment [12].…”

Section: Introductionmentioning

confidence: 99%

Fast orthorectification method using multi-pixel chunking

Zhou

Wang

2023

SPIE-CLP Conference on Advanced Photonics 2022

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The traditional digital differential orthorectification uses a pixel-by-pixel correction method, which is time-consuming and laborious to operate on a single image element and fails to meet the demand for time-sensitive and near real-time DOM production generation. In order to solve this problem, a fast DOM generation method based on multi-pixel binning is investigated in this paper. The method breaks the shackles of the traditional digital differential correction, which only operates on individual pixels, and instead corrects blocks of N×N size, greatly improving the speed of DOM generation processing. The dataset used in this paper are aerial imagery and a DSM of LiDAR acquisition located in Denver, Colorado, USA, to verify the speed and accuracy of the algorithm proposed. Compared with the traditional digital differential correction method, the speed of DOM generation using this method is dependent on the pixel chunk size. The orthorectification speed about 4.1 times faster than the traditional method when using a 3×3 pixels chunks, and about 9.8 times faster than the traditional method when using a 7×7 pixels chunks.

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Section: Introductionmentioning

confidence: 99%

Fast orthorectification method using multi-pixel chunking

Zhou

Wang

2023

SPIE-CLP Conference on Advanced Photonics 2022

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“…Tomasi et al [ 18 ] proposed a stereo vision algorithm using an FPGA to perform the correction of video graphics array images (57 fps). Pal et al [ 19 ], Wang et al [ 20 ], and Zhang et al [ 21 ] applied FPGAs to accelerate the image data and signal filtering processes. Ontiveros-Robles et al [ 22 , 23 ] proposed FPGA-based hardware architectures for real-time edge detection using fuzzy logic algorithm.…”

Section: Introductionmentioning

confidence: 99%

RPC-Based Orthorectification for Satellite Images Using FPGA

Zhang

Zhou

Zhang

et al. 2018

Sensors

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Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.

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“…In recent decades, the field programmable gate array (FPGA) has been widely used in the image processing (such as imaging compression [7,8], filtering [9][10][11], edge detection [12,13], real-time processing of video images [14,15], and motion estimation [16][17][18]) to make real-time processing come true. González et al [16,17] optimized matching-based motion estimation algorithms using an Altera custom instruction-based paradigm and a combination of synchronous dynamic random access memory (SDRAM) and on-chip memory in Nios II processors, and presented a low-cost system.…”

Section: Introductionmentioning

confidence: 99%

On-Board Ortho-Rectification for Images Based on an FPGA

et al. 2017

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Abstract:The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique based on a field programmable gate array (FPGA) platform that can be implemented on board spacecraft for (near) real-time processing. The proposed FPGA-based ortho-rectification method contains three modules, i.e., a memory module, a coordinate transformation module (including the transformation from geodetic coordinates to photo coordinates, and the transformation from photo coordinates to scanning coordinates), and an interpolation module. Two datasets, aerial images located in central Denver, Colorado, USA, and an aerial image from the example dataset of ERDAS IMAGINE 9.2, are used to validate the processing speed and accuracy. Compared to traditional ortho-rectification technology, the throughput from the proposed FPGA-based platform and the personal computer (PC)-based platform are 11,182.3 kilopixels per second and 2582.9 kilopixels per second, respectively. This means that the proposed FPGA-based platform is 4.3 times faster than the PC-based platform for processing the same RS images. In addition, the root-mean-square errors of the planimetric coordinates ϕ X and ϕ Y and the distance ϕ S are 1.09 m, 1.61 m, and 1.93 m, respectively, which can meet the requirements of correction accuracy in practice.

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FPGA implementation of the coupled filtering method

Cited by 3 publications

References 21 publications

Fast orthorectification method using multi-pixel chunking

Fast orthorectification method using multi-pixel chunking

RPC-Based Orthorectification for Satellite Images Using FPGA

On-Board Ortho-Rectification for Images Based on an FPGA

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