Image contrast enhancement by automatic multi-histogram equalization for satellite images

A, Pugazhenthi; Kumar, Lakshmi Sutha

doi:10.1109/icscn.2017.8085722

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…It is not suitable for hardware-based modeling because of design complexity. The Pugazhenthi et al [20] presents the automatic multi-histogram equalization as an image enhancement technique for satellite images using Matlab programming, which prevents the brightness and improve the contrast. The image quality results are not so appropriate and not suitable for hardware-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Imaging Chip using High-Speed AXI-Interface for MPSOC Applications on FPGA Platform

Archana,

Reddy

2024

Wireless Pers Commun

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The recent innovations in real-time video and image enhancements are allowing much advancement in a wide range of diverse applications. These innovations and advancements provide a new hardware architecture which aims to improve the image visualization, processing speed, and complexity reduction in hardware. The imaging chip concept is introduced in this article to support the Multiprocessing system on chip (MPSoC) applications in real-time scenarios on a single chip. The imaging chip model is designed using high-speed interface protocol, which includes different image enhancement algorithms acts as a master model, Advanced Extensible Interface (AXI)-4 as an interface model, and dual-port memory as a slave model. The image enhancement algorithm includes mainly, Brightness control, contrast stretching, Adaptive median filtering (AMF), Edge-detection techniques, image Thresholding, and Image Histogram method. The AXI-4 provides a high-speed interface for communicating master and slave modules. The proposed model work based on the modes of the operation to process the enhanced image output in MPSoC. The design supports multiple masters and multiple slave modules with reconfigurable nature. The imaging chip is a module on the Xilinx ISE environment and implemented on Artix-7 FPGA, along with the performance metrics like chip Area, time, power, and memory utilization are analyzed with improvements. The model offers low latency and high throughput architecture for real-time Multimedia applications.

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Section: Related Workmentioning

confidence: 99%

Design and Analysis of Imaging Chip using High-Speed AXI-Interface for MPSOC Applications on FPGA Platform

Archana,

Reddy

2024

Wireless Pers Commun

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“…The enhancement is done using both direct pixel approaches and indirect pixel techniques. In [6] Introduced image enhancement and extraction of functionality depend on satellite data. The application of texture estimates and transformation properties based on the visual intensity obtains high-resolution satellite imagery while using different image enhancement systems is primarily discussed.…”

Section: Prior Workmentioning

confidence: 99%

High-Quality Image Enhancement Algorithm Using Kinetic Gas Molecular Optimized (KGMO) Hyperbolic Tangent Function

2022

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One of the main steps in various digital image-based applications such as classification, segmentation and prediction etc., is to improve the visibility of digital images. In order to improve the visual efficiency of images, contrast enhancement (CE) is an important preprocessing technique. Depending on the capturing environment, the quality of the image varies, and CE is an important approach to improve the image quality. However, excessive CE resulting in loss of detail and an unnatural appearance of the target image. This paper proposes a new CE using the Kinetic Gas Molecular Optimized Hyperbolic Tangent Function (KGMO-HTF). This is used here to optimize the brightness of the picture α and the sensitivity parameter of the brightness esteems β with hyperbolic tangent (tanh) function. Different parameters such as Enhancement Mean Error (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (DE) are measured to evaluate efficiency of proposed method. Experimental findings are illustrated by CE-KGMO-HTF methods that perform better than conventional methods of CE.

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“…12 Go to step 9 and move to next pixel and construct another window. 13 Regarding the details of three important steps of performing LFIT after selection appropriate block size and Sd and Se parameters as follows:…”

Section: Local Fuzzy Inference Techniquementioning

confidence: 99%

“…In terms of image contrast enhancement using fuzzy techniques, these techniques inhibit noise and intensify contrast because they optimize parameters of membership functions based on the highest value of fuzzy entropy and modify membership equation based on these parameters [10][11][12]. They have also been performed efficiently on many kinds of images, such as medical x-ray images [10], breast ultrasound images [13], musculoskeletal ultrasound images [14] and satellite images [15]. Furthermore, Anatomical constrained neural networks (ACNN) method which was powerfully applied on 3d cardiac ultrasound images to predict and improve accuracy [16], but this kind of neural networks require a large set of images for training.…”

Section: Introductionmentioning

confidence: 99%

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Jabbar¹,

Aladi²,

Day³

et al. 2021

Biomed. Phys. Eng. Express

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Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference system. Local Fuzzy Inference Technique (LFIT) was introduced as a novel technique to enhance the contrast of MUI. The input data used musculoskeletal ultrasound images were collected from healthy volunteers. Local Fuzzy Inference Technique (LFIT) was compared with a recent fuzzy technique of the image enhancement and validated based on assessment metrics (second-derivative-like measure of enhancement (SDME)). The results advocated an improved quality of the musculoskeletal ultrasound images based on the LFIT technique with approximately 11% greater than recent technique and computation time of LFIT is 28.4% is less. It is possible to apply a proposal technique on the other types of image (panoramic image and video). Furthermore, observed improvements on the MUI quality could potentially invested as a pre-processing step before performing other computer vision applications, such as image segmentation, tracking, and 3D image reconstruction.

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Image contrast enhancement by automatic multi-histogram equalization for satellite images

Cited by 14 publications

References 9 publications

Design and Analysis of Imaging Chip using High-Speed AXI-Interface for MPSOC Applications on FPGA Platform

Design and Analysis of Imaging Chip using High-Speed AXI-Interface for MPSOC Applications on FPGA Platform

High-Quality Image Enhancement Algorithm Using Kinetic Gas Molecular Optimized (KGMO) Hyperbolic Tangent Function

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

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