Performance of Various Order Statistics Filters in Impulse and Mixed Noise Removal for RS Images

Radhika, V.; Padmavathi, G.

doi:10.5121/sipij.2010.1202

Cited by 5 publications

(3 citation statements)

References 3 publications

(3 reference statements)

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“…This difference is due to randomness or because the estimator doesn't account for the information that could produce a more accurate estimate. Lower value of MSE indicates better image quality [6], [7].…”

Section: Methodsmentioning

confidence: 99%

“…The basic difference between these two transforms, is in the construction of the window function which has a constant length in the case of STFT (including rectangular, Blackman and other window functions). The basic insight behind denoising is that, it tries to keep transform coefficient of high PSNR (Peak Signal-to-Noise Ratio) while zeroing out coefficients having lower PSNR [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Major Image Enhancement Algorithms

Kulkarni¹,

Rangaswamy²,

Manonmani³

2017

EJERS

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Image restoration is a process of reconstruction or recovery of an image that has been corrupted or degraded by any degradation phenomenon. Image restoration techniques are inclined towards modeling the degradation and applying the inverse process in order to recover the original image. The critical goal of restoration techniques is to improve the quality of an image in some predefined manner. This present paper is a comparative study of image enhancement techniques used for improving the quality of a given image and evaluate it against the quality of a given image and evaluate it against SNR, PSNR, MSE, and SSIM as metrics.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Study of Major Image Enhancement Algorithms

Kulkarni¹,

Rangaswamy²,

Manonmani³

2017

EJERS

View full text Add to dashboard Cite

show abstract

“…2) will produce low complexity edge preserved smooth maximum intensity. This maximum intensity channel will be treated as the TM t(x) [4,[17][18][19][20][21][22][23][24][25][26][27][28][29]. Whereas this proposed concept is computationally simple and easy to implement.…”

Section: Lms-based Improved Depth Information Estimationmentioning

confidence: 99%

RLaMs-Dehazing: Optimized Depth Map Improvement Single Colour Image Dehazing

Roy

2023

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Visibility Degradation is a classical problem owing to the presence of Atmospheric Particulate Matter (APM). There are different image dehazing algorithms. Any one method cannot be relied upon as each haze condition is unique. An innovative algorithm has been proposed inverting the image formation atmospheric scattering model [2, 32]. The model has been improvised by one key factor. This is Regularized Lagrangian multiplier (RLaM) based Depth Map (DM) refinement. The algorithm has low time complexity which intrigues real-time efficient applications. Different state-of-the-art visibility algorithms have been studied and their subjective and objective performance evaluations have been evaluated. Extensive investigation shows remarkable improvement with the proposed algorithm. This method is equally applicable to different atmospheric conditions. Time complexity experimented with execution time and Big (O) for real-time effectiveness. Extensive experiment results show the potential of the proposed algorithm independent of the influence of atmospheric conditions and capturing devices adaptive to computer vision applications. Time complexity and quality output trade-off achieved with the removal of ringing artifacts efficiently.

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Motion detect application with frame difference method on a surveillance camera

Husein

Calvin

Halim

et al. 2019

J. Phys.: Conf. Ser.

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Security becomes one of the major necessities in our lives nowadays however criminal activities are still at large with criminals unable to be persecuted without eligible proofs of their misdeeds. Surveillance Camera is one of the better solutions to these problems in which they can be positioned at every corner of a building even streets and alleys. Their functions can be enhanced by adding algorithms that can identify objects. Frame Differences method is an algorithm to identify an object’s motion. Using this algorithm, we could differentiate an object moving in the environment. Background subtraction is one of the methods suitable to further improve frame differences thus increasing its effectiveness and precision. After implementing the method on a camera, the luminosity was founded to influence the threshold value significantly, the threshold value of 35 is the optimal value.

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Performance of Various Order Statistics Filters in Impulse and Mixed Noise Removal for RS Images

Cited by 5 publications

References 3 publications

Comparative Study of Major Image Enhancement Algorithms

Comparative Study of Major Image Enhancement Algorithms

RLaMs-Dehazing: Optimized Depth Map Improvement Single Colour Image Dehazing

Motion detect application with frame difference method on a surveillance camera

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