基于fpga的红外探测器角位置红外图像消模糊外文翻译资料

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毕业设计(论文)

外文资料翻译

fpga-based infrared image deblurring using

angular position of IR detector

Tugay Doner·Dincer Gokcen

Abstract

The motion of the object or the infrared (IR) imaging system during the integration time causes blurring of the IR image.This study covers real-time field programmable gate array (FPGA)-based deblurring for IR detectors, and an inertial meas-urement unit (IMU) was used to quantify the blur caused by the IR detector movement. Point spread function for each pixelwas calculated using the angular position data of the IR detector obtained from IMU. Both spatially invariant and spatiallyvariant blur cases can be modeled for the IR detector motion. After the quantification, the spatially invariant-type blur was eliminated using a Wiener filter-based deblurring algorithm. Deblurring algorithm was implemented in the Xilinx system generator environment directly using FPGA IP cores. The simulation results in the Xilinx system generator environment indicate that the proposed image deblurring method is real-time applicable, and it reduces the processing time of a single frame to 4 ms. For the implementation of 2D-fast Fourier transform design in FPGA using the corner turn matrix method, memory management is the most critical factor influencing the speed. The real-time deblurring solution given herein has the potential to be used in IR cameras on the moving platforms to increase the performance and robustness in systems such as object tracking and visual navigation.

Keywords Infrared imaging·Blur·FPGA·Point spread function·IMU

1 Introduction

Image blur is one of the most critical problems in the infra-red (IR) imaging systems. This study focuses on the motion blur resulting from the movement of the infrared imaging system. The platforms in which the imaging system is mounted are usually not stationary. There is a conventional mechanism called gimbal to stabilize the IR imaging system. A very ordinary gimbal mostly consists of electrical motors on each axis, some feedback sensors, and drive circuits. In a target tracking schema using the infrared signature of an object, a gimbal system is used to make proper IR detecfor orientations to keep the target within the field-of-view (FOV) of the imaging system. The performance of gimbals might be problematic in certain conditions such as sudden platform movement, vibration, mechanical shock, and some attack and avoidance maneuvers. At this point, the decrease in the integration time may be a solution to capture the clear images, but this time target visibility decreases proportionally with the integration time. During relatively long integration times, the stability of the imaging platform is critical not to acquire a blurred infrared image. Especially for a visual navigation algorithm, image blurring can result in drastic errors. Real-time deblurring draws the attention of IR imaging to improve the performances of navigation, guidance, surveillance, and other imaging purposes.

The first studies on image deblurring date back to the 1960 s. Richardson used Bayesian theory and conditional probability principles to enhance degraded images as their point spread functions (PSF) are known [1]. Methods such as inverse filtering, Wiener filtering, iterative least-squares filtering, many solutions based on energy minimization, and variational Bayes methods are used extensively for deblurring, and they are the basis for the current studies [2]. There are different alternating approaches for regularization. One of them is used the geometric deformation for deconvolution [3]. Yang et al. [4] used a rolling bilateral filter-based regularization for text image deblurring. Statistical data, such as natural image gradients distributions, are also commonly used for deblurring tasks. There are two main approaches for image deblurring: probabilistic methods and deterministic methods. Deterministic methods cover inverse filtering and least-squares, obtained by direct analytical or numerical calculations, in which random or probabilistic operations do not take place [5, 6]. Some iteration-based deblurring studies could be included in this class [7]. The probabilistic methods are Wiener filtering, a posteriori solution, and iterative Kalman filter approaches [8]. All methods relying on statistical data can be considered as a probabilistic approach.

There are also some hardware-based studies focusing on image deblurring. Yuan et al. [9] worked on obtaining a clear image by using a noisy and blurred image pair of the original image. Using a fluttered shutter is another approach introduced to be a solution for image deblurring [10]. Joshi et al. [11] used an ordinary inertial measurement unit (IMU) for estimating the trajectory of the camera motion during the exposure time. In addition to the daylight cameras, there are various studies in the literature focusing on the deblurring of IR images. Oswald Tranta et al. [12, 13] deblurred the infrared images taken from a micro-bolometer infrared detector by using a parametrized Wiener filter method. They assumed

the infrared images blurred due to the moving object, and PSF of motion blur is an exponential decay function. For an accurate PSF, the speed and direction of the object should be known. Oswald Tranta [14] also worked on obtaining an accurate temperature measurement by deblurring the infrared images. Wang et al. [15] used the iterative Wiener filter to estimate the PSF filter of a motion-blur in an infrared image. Some studies provide image deblurring approaches

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