AI Based Automated Identification and Estimation of Noise in Digital Images

Karibasappa, K.

doi:10.1007/978-3-319-11218-3_6

Cited by 4 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed that neural networks were more effective at detecting noise. The exact filter can be used to enhance the given images by recognizing the noise [33].…”

Section: Literature Surveymentioning

confidence: 99%

“…An approach based on neural networks for detecting noise types in noisy images is presented by Karibasappa K.G and colleagues [15]. There are many types of de-noising filters that can be applied to the proposed method.…”

Section: Literature Surveymentioning

confidence: 99%

“…A noise model study is essential for image noise removal, which is required for better accuracy results [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

View full text Add to dashboard Cite

Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery.

show abstract

“…The results showed that neural networks were more effective at detecting noise. The exact filter can be used to enhance the given images by recognizing the noise [33].…”

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Although it is important to be able to eliminate noises, it is equally significant, if not more crucial, to characterise the type and level of the noises present in the images based on its nature and distribution. To the best of our knowledge, only [4, 15–17] attempted to address this issue. For instance, Karibasappa and Karibasappa [16] employed probabilistic neural network (PNN) to identify images affected by noises by extracting the statistical features and adopted fuzzy logic concepts to estimate the noise level.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, only [4, 15–17] attempted to address this issue. For instance, Karibasappa and Karibasappa [16] employed probabilistic neural network (PNN) to identify images affected by noises by extracting the statistical features and adopted fuzzy logic concepts to estimate the noise level. A neural network approach with moments‐extracted feature sets technique by Vasuki et al [17] is able to identify image noises.…”

Section: Introductionmentioning

confidence: 99%

Image noise types recognition using convolutional neural network with principal components analysis

et al. 2017

View full text Add to dashboard Cite

This study presents a model to effectively recognise image noise of different types and levels: impulse, Gaussian, Speckle and Poisson noise, and a mixture of multiple types of the noise. To classify image noise type, the convolutional neural network (CNN) method with backpropagation algorithm and stochastic gradient descent optimisation techniques are implemented. In order to reduce the training time and computational cost of the algorithm, the principal components analysis (PCA) filters generating strategy is deployed to obtain data adaptive filter banks. The authors validated their designed CNN with PCA for noise types recognition model with degraded images containing noise of single and combination of multiple types, with a total of 11,000 and 1650 datasets for training and testing purposes, respectively. The variety and complexity of data have never been addressed before in any other research work. The capability of their intelligent system in handling images degraded under this complicated environment has surpassed human‐eye performance in noise types recognition. The authors’ experiments have proven the reliability of the proposed noise types recognition model by having achieved an overall average accuracy of 99.3% while recognising eight classes of noise.

show abstract

Image Noise Recognition Algorithm Based on Improved DenseNet

Huang

Zeng

et al. 2021

Web Information Systems and Applications

View full text Add to dashboard Cite

AI Based Automated Identification and Estimation of Noise in Digital Images

Cited by 4 publications

References 14 publications

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Image noise types recognition using convolutional neural network with principal components analysis

Image Noise Recognition Algorithm Based on Improved DenseNet

Contact Info

Product

Resources

About