FPGA implementation of image enhancement technique for Automatic Vehicles Number Plate detection

Shandilya, Rahul; Sharma, R. K.

doi:10.1109/icoei.2017.8300860

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The system can perform super-resolution between 960 × 540 and 1920 × 1080 at 60 fps. Shandilya et al [ 30 ] applied image enhancement based on FPGA to the automatic vehicles number plate (AVNP) problem and achieved image enhancement by estimating the best value of various aspects of image quality. Prashant et al [ 31 ] processed blurred or useless image information on a FPGA SPARTAN 3 XC3S500E board and obtained high-quality images by image fusion.…”

Section: Related Workmentioning

confidence: 99%

A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Sun

Gao

et al. 2021

Entropy

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Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

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Section: Related Workmentioning

confidence: 99%

A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Sun

Gao

et al. 2021

Entropy

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“…Our objective at this phase is to identify the Number Plate area from the original image utilizing edge-based image processing scheme. To handle this various method are developed for image enhancement like morphological methods, Canny edge detection, OCR etc [14]. Optical character recognition OCR is used for number plate area determination.…”

Section: Number Plate Area Determinationmentioning

confidence: 99%

“…Comparison of implemented architecture design with existing design S. S. Omran and J. A. Jarallah in[14] reported a system of automatic license plates identification among three different style Iraqi car license plate. Three styles of license plate were distinguished by their plate size.…”

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confidence: 99%

A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

Khan¹,

Yadav²,

Agarwal³

2018

IJET

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Licensed Number plate recognition plays vital role in smart cities for maintaining Law & Order and traffic management. NPR based system mainly involves four stages namely 1) Image capture & Pre-Processing 2) Number plate area determination 3) Character Segmentations 4) Recognition of all character. This survey paper extensively analyzed the method of extraction of number plate, its platform, performance and execution time. With the development of Multilayer Perceptron Network accuracy and time in image processing has been achieved up to a great instant. Hence this analysis will help the precise assessment in establishing research and enable developers to assess which strategies are aggressive in present environment.

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A study on VLSI implementation of image enhancement techniques

Aklak

Pugazhenthi²,

Alex

2021

Concurrency and Computation

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In real-time, the images captured are not always appreciable for visual perception.The quality of the image relies on the illumination condition. Contrast enhancement is required to improve the visual quality of the image. In this article, the image enhancement techniques that are adaptable for real-time images are studied. Further, an investigation is done on implementing the image processing algorithms in very large-scale integration (VLSI) hardware. As the image data are comparatively larger, the computational complexity and the memory requirement are more. From the study, the need for efficient computation of such image processing algorithms is realized. The efficient computation of image processing algorithms on hardware platforms, in particular on field programmable gate array (FPGA), is reviewed. As an experiment, the gamma-transformation technique used for image enhancement is realized. Initially, the prototype is modeled using MATLAB, and then it is implemented using an FPGA. K E Y W O R D Sfield programmable gate array, floating point multiplier, image enhancement INTRODUCTIONIn the modern digital world, all multimedia transactions require good-quality images. The visibility of an image depends on the illuminance and reflectance of the scene in the real world. The brightness of a point in an image is non-linear to the brightness of the same point in the real world.Generally, lighting condition determines the quality of an image. The images taken under poor lighting conditions may cause discomfort while viewing images.The image acquired can be degraded due to various reasons such as sensor level malfunction, low visibility, excessive exposure, foggy conditions, and so on. 1 To overcome the degradation, pre-processing is required to enhance the image. Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further processing. It includes processes like deblurring, noise removal, filtering, and so on.Not all images will require the same image enhancement technique because there may be over-exposed regions in outdoor images that will need different treatment compared to indoor images. In literature, there are many algorithms proposed for image enhancement. The technique of being applied for enhancement depends on the input quality of the image. The quality needs to be assessed before applying any approach for a better perception of the image. A real-time implementation of image enhancement is hard to design using software because a large data set is required to represent an image. Since field programmable gate arrays (FPGAs) could exploit the spatial parallelism in image enhancement algorithms they are widely used for hardware implementation.Realizing the design on FPGAs, offer a compromise between the flexibility of general-purpose processors and the hardware-based speed of an integrated-circuit design. 2 Also, the programmable property of FPGA makes them a good choice for image processing applications, as the processing of the number of images also...

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FPGA implementation of image enhancement technique for Automatic Vehicles Number Plate detection

Cited by 8 publications

References 7 publications

A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

A study on VLSI implementation of image enhancement techniques

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