Design and FPGA implementation of an 2D Gaussian surround function with reduced on-chip memory utilization

Hanumantharaju, M. C.; Ravishankar, M.; Rameshbabu, D. R.

doi:10.1109/icacci.2013.6637241

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Twodimensional Gaussians are also used in other scientific disciplines such as physics (Kravtsov & Berczynski, 2004;Z. Wu & Guo, 1998), materials sciences (Riekel et al, 1999), and image processing (Hanumantharaju et al, 2013;Ketenci & Gencturk, 2013), particularly in medical imaging (Qadir et al, 2021;J. Wu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Baliga¹

2021

JOSS

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Section: Discussionmentioning

confidence: 99%

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Baliga¹

2021

JOSS

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“…Among such concepts, the single-scale retinex (SSR) model proposed by Jobson et al [24] was examined because it involves simple calculations and improves the illumination of images. In brief, the SSR model works by estimating an illumination image from its degraded counterpart by performing a discrete convolution (*) between a degraded image and a discrete 2D Gaussian surround function (DGSF) [25]. www.ijacsa.thesai.org 1) RBMA [26]: Mohammad Abid et al [26] proposed RBMA which involves in determining the log of the illumination and the original images followed by computation of GCS (Gama Corrected Sigmoid function) .The enhanced output of Retinex-based Multiphase Algorithm with different values of γ is as shown in the Fig.…”

Section: B Retinex Based Algorithmmentioning

confidence: 99%

Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

Subash¹,

Majumdar²

2022

IJACSA

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Computer vision has its numerous real-world applications on Visual Object Tracking which includes humancomputer interaction, autonomous vehicles, robotics, motionbased recognition, video indexing, surveillance and security, human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. The factors affecting the tracking process is due to low illumination, haze and cloudy environment and noisy environment. In this paper, we aim to extensively review the latest trends and advances in adaptive enhancement algorithm and evaluate the performance using Full reference like, SSIM

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“…The log of the output of this operation is taken then subtracted from the log of the degraded image to produce the reflectance image R(x,y) which represents an enhanced observation of the degraded image [24]. More specifically, to apply the SSR model on a degraded image, the DGSF is first computed as follows [25]:…”

Section: Proposed Algorithmmentioning

confidence: 99%

Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

Al-Hashim¹,

Al-Ameen²

2020

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These days, digital images are one of the most profound methods used to represent information. Still, various images are obtained with a low-light effect due to numerous unavoidable reasons. It may be problematic for humans and computer-related applications to perceive and extract valuable information from such images properly. Hence, the observed quality of low-light images should be ameliorated for improved analysis, understanding, and interpretation. Currently, the enhancement of low-light images is a challenging task since various factors, including brightness, contrast, and colors should be considered effectively to produce results with adequate quality. Therefore, a retinex-based multiphase algorithm is developed in this study, in that it computes the illumination image somewhat similar to the single-scale retinex algorithm, takes the logs of both the original and the illumination images, subtract them using a modified approach, the result is then processed by a gamma-corrected sigmoid function and further processed by a normalization function to produce to the final result. The proposed algorithm is tested using natural low-light images, evaluated using specialized metrics, and compared with eight different sophisticated methods. The attained experiential outcomes revealed that the proposed algorithm has delivered the best performances concerning processing speed, perceived quality, and evaluation metrics.

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Design and FPGA implementation of an 2D Gaussian surround function with reduced on-chip memory utilization

Cited by 9 publications

References 6 publications

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

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