Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

Su, Ting‐Wei; Cheng, Jui Chuan; Lin, Yu Jen

doi:10.4028/www.scientific.net/amr.422.771

Cited by 1 publication

(1 citation statement)

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“…The Gaussian noise is not considered for the evaluation. A CNN‐based noise removal technique for color images have been proposed with hybrid linear matrix inequality and swarm optimization procedure [21]. The cloning templates were designed for salt and pepper noise, speckle noise, and Gaussian noise, and noise removal has been evaluated for color images.…”

Section: Introductionmentioning

confidence: 99%

Two‐dimensional DFT with sliding and hopping windows for edge map generation of road images

Vishwakarma

Sukumar

Sumathi

2021

IET Image Processing

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An edge map generation technique based on two-dimensional discrete Fourier transform with sliding and hopping windows is proposed for road images to detect the lanes and road markings. A 2 × 2 sliding/hopping window DFT with bin indices (k 1 , k 2 = 0, 1) for horizontal edge detection and (k 1 , k 2 = 1, 0) for vertical edge detection has been proposed. The 2-D SDFT/HDFT-based edge detector has been proved to be more efficient for lane and road marking images in comparison with conventional edge detectors and cellular neural network based edge detectors. In the presence of noise and various signal to noise ratio conditions, the horizontal and vertical edges have been efficiently recovered with good Pratt figure of merit without applying any pre-processing, noise removal process, and post processing techniques. The PFOM was found to be quite stable with wide threshold range for various noise levels. The consistent performance of the proposed edge detector is proved with MSE and PSNR determination of detected edge images. Moreover, the proposed 2-D SDFT/HDFT-based edge detector performs well in developing edge maps for real-time road videos. The system-on-chip implementation of the 2-D SDFT/HDFT edge detector on Cyclone IV FPGA chip is also carried out for detecting the lane and road markings. 1 INTRODUCTION Vision based measurement (VBM) is being proposed and used in various automated applications such as advanced driver assistance systems (ADAS) and intelligent transportation systems [1, 4]. Camera-based vehicle instrumentation is becoming popular to analyze the intentions and state of a driver, and detect potential driver errors to significantly reduce car accidents [2]. The ADAS integrates a number of different functions such as lane change assist, forward and rear collision warning, park assist, and blind spot detection [3]. Edge detection is an important process for the lane position detection, tracking systems, advanced driver assistant systems, and intelligent transportation systems [4]. Lane detection is a two-step process, in which the first step is detecting the lanes and then fit to the parametric curve. The challenges involved in vision-based lane detection include the lack of clarity of lane markings, light reflections, illumination, shadows, poor visibility due to bad weather conditions and high noise levels. Due This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%