Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Khmag, Asem; Ghoul, Sami; Al-Haddad, S. A. R.; Kamarudin, Noraziahtulhidayu

doi:10.12928/telkomnika.v16i2.9060

Cited by 8 publications

(5 citation statements)

References 25 publications

(16 reference statements)

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“…Thereupon, self-regulating computer-abetment investigation approach [14] placed on SVM designation for MRI brain illustrations was invented in 2017. Next, the outlier suppression approach [15] placed on outlier consistency evaluation by local statistics was invented for illustrations in 2018. Succeeding, the outlier suppression approach [16] placed on a powerful filtering approach was invented for color illustrations in 2018.…”

Section: Introductionmentioning

confidence: 99%

An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

Patanavijit

Thakulsukanant

2022

Bulletin EEI

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Due to large current applications on digital images in these recent years, outlier suppression is one of the primary stages for modern computer vision implementations thereupon there are tremendously invented for creating an efficient and practical outlier suppression, which ordinarily are composed of outlier recognition stage and outlier rebuilt stage. The localised rank ordered difference (LROAD) approach, which is progressed from rank-ordered absolute differences (ROAD), has been invented since 2016. Later, the LROAD approach evolved to be one of the efficient outlier recognition stages from its eminent effectiveness. The paper focus to propose the innovative vigorous outlier recognition placed on localised rank-ordered logarithmic differences (LROLD) approach, which is progressed from LROAD and rank-ordered logarithmic differences (ROLD), which is higher effectiveness than the ordinary LROAD, for applying on FAIN. From the computer experiments, which are examined on many depictions such as Girl, Pepper F16 and Lena, the innovative vigorous outlier recognition placed on LROLD approach has higher eminent effectiveness then the stage-of-art approach such as LROAD and ROAD approaches at numerous consistencies of FAIN.

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Section: Introductionmentioning

confidence: 99%

An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

Patanavijit

Thakulsukanant

2022

Bulletin EEI

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“…Thereupon, an alternative irregularity reduction algorithm [14] built on self-regulating computer-abetment investigation scheme with support-vector machines (SVM) scheme for engaging on magnetic resonance imaging (MRI) brain digital depictions was formulated in 2017. Succeeding, an irregularity reduction algorithm [15] built on irregularity consistency classification by neighborhood statistic scheme was formulated in 2018. In 2018, an irregularity reduction algorithm [16] built on an efficient filtering scheme was formulated for engaging on color digital depictions.…”

Section: Introductionmentioning

confidence: 99%

The alternative irregularity reduction algorithm built on 2-stage identification with AMF on FMIO

Patanavijit

Kesrarat

Thakulsukanant

2022

IJEECS

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Built on neighborhood correlation, a 2-stage identification technique was offered to incorporate on the irregularity reduction algorithm for random magnitude impulse outlier (RMIO). As a result, this algorithm has an ultimate efficacy thereby a 2-stage identification technique becomes to be one of the high efficacy identification technique. Accordingly, this paper attempts to propose an alternative irregularity reduction algorithm built on a 2-stage identification and adaptive median filter (AMF) with under fix magnitude impulsive outlier (FMIO) at little, mild and immense massiveness. First, by examining great number of depictions, the optimized window dimension for 2-stage scheme from computation and performance is disposed. Second, comprehensive examinations represent that the 2-stage identification technique is disposed to identify between regular and irregular pixels at all massiveness, especially little and mild massiveness. Third, the identification efficacy on great depictions at all massiveness is examined on regular, irregular, regular-irregular efficacy perspective to estimate the optimal window size and optimal 2-stage constant value. Finally, the overall outlier reduction efficacy of an outlier reduction built on 2-stage technique and AMF is examined on great depictions at all massiveness related with other up-to-the-minute outlier reductions. From these results, the outlier reduction has remarkable efficacy than other up-to-the-minute outlier reductions.

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“…Basically, digital image processing produces functions of light intensity that are represented in two dimensions [3]. Each image from the real world has various characteristics, which also consist of various knowledge [4]. For this reason, images need to be classified in order to be recognized and understood by computers quickly [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Syah

Al-Khowarizmi

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Digital image processing, in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides, classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using Mean Absolute Percent Error (MAPE) for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of Artificial Neural Network (ANN) is very suitable for optimizing classification using the Simple Evolving Connectionist System Method (SECoS), and as a result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.

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Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Cited by 8 publications

References 25 publications

An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

The alternative irregularity reduction algorithm built on 2-stage identification with AMF on FMIO

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

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