A Novel Approach to Classify Noises in Images Using Artificial Neural Network

Santhanam,

doi:10.3844/jcssp.2010.506.510

Cited by 15 publications

(5 citation statements)

References 9 publications

(7 reference statements)

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“…For noise identification, an Artificial Neural Network (ANN)-based technique was presented [32]. The proposed method involved isolating the noise samples and extracting their statistical properties, which were then used to identify the noise using a neural network.…”

Section: Literature Surveymentioning

confidence: 99%

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

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

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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.

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Section: Literature Surveymentioning

confidence: 99%

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

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

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“…Testing on the X-ray images validates the effectiveness of the new histogram. Santhanam and Radhika (2010) have proposed a technique Noise identification predominant in any digital image processing algorithms, which helps in identifying the filters to smooth the image for further processing. In this proposed method, an Artificial Neural Network (ANN) based approach was proposed for noise identification.…”

Section: Related Researches: a Reviewmentioning

confidence: 99%

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Babu¹,

Sunitha²

2015

Journal of Computer Science

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Abstract:This study presents an image enhancement approach to Cuckoo Search Algorithmin with Morphological Operation. At the present time, in many image processing applications digital images are developed. Machine vision, computer interfaces, manufacturing, compression for storage and more are some of the fields of image processing application. Before using it in any applications the image has to be managed, such processing is said to be image enhancement. We propose a method to combine with an enhancing digital images through cuckoo search algorithmin and morphological operation. Therefore, the appearance of noise produces distortion in an image and thus the image will be unattractive. This decreases the discernibility of many features inside the images. In this study, we are working to overcome this drawback by getting an improved contrast value after converting the color image into grayscale image. The fundamental characteristic of this CS algorithm is that the amplitudes of its components can objectively reflect the contribution of the gray levels to the representation of image information for the best contrast value of an image. After selecting the best contrast value of an image in CS algorithm, morphological operations have to be done. In morphological operations, the intensity parameters of the image are adjusted to improve its quality. Experimental results demonstrate that the proposed approach is converted into original color image without noise and adaptive process to enhance the quality of images.

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“…Their work is effective for the determination of homogeneous regions in an image, and is a significant improvement in field of noise identification and estimation. Santhanam and Radhika [15] employed a novel approach to classify noises by using three additive noises namely Salt & Pepper, Non Gaussian white & Gaussian white for comparative study of classification using Back Propagation Network and Multi Layer Perceptron. In this research, we extend the previous works with additive and multiplicative noises both.…”

Section: Introductionmentioning

confidence: 99%

Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network

Tiwari¹,

Singh²,

Shukla³

2011

IJCA

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A neural network classification based noise identification method is presented by isolating some representative noise samples, and extracting their statistical features for noise type identification. The isolation of representative noise samples is achieved using prevalent used image filters whereas noise identification is performed using statistical moments features based classification system. The results of the experiments using this method show better identification of noise than those suggested in the recent works.

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A Novel Approach to Classify Noises in Images Using Artificial Neural Network

Cited by 15 publications

References 9 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

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network

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