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

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## Abstract

**:**

## 1. Introduction

## 2. Literature Survey

- For removing noise from the images, it is necessary to detect the type of noise so that the content of the image will not get hampered while removing noise pixels.
- Also, there is a need to develop system in such a way that irrespective of the content of the image the type of noise should get detected.

Paper | Type of Detection | Technique Used | Dataset | Accuracy (%) |
---|---|---|---|---|

[9] | Classification of murals | MultiChannel seperable network model (MCSN) | China Dunhuang Murals | 88.16 |

[16] | Biological images | Deep Learning | Wood boards | 93 |

[18] | COVID-19 | deep CNN-LSTM | X-ray images | 99.4 |

[37] | Living or non living things | VGG16 | ImageNet | 99.8 |

[38] | Cloud shape | CNN and FDM | 200 actual photos of real scenes a | 94 |

[39] | Image detection | Two stage training | USPS, ILSVRC2012, MNIST, SVHN, CIFAR10, CIFAR100 | 98 |

Proposed System Architecture | Noise type | Wavelet Transform and CNN | Ancient mural images | 99.25 |

## 3. Proposed Noise Identification

## 4. Algorithm Steps and Processes

**Step 1:**Acquire ancient images from dataset

**Step 2:**Decompose image using wavelet transform and extract the features [43].

**Step 3:**Dimensional reduction of features

**Step 4:**Pass the features to Convolutional Neural Network

**Step 4:**Pass the features to Convolutional Neural Network

**Step 5:**Flattening of the pooled features

**Step 6**: Noise classification

## 5. Results and Discussion

#### 5.1. Comparative Methods

#### 5.2. Comparative Analysis

#### 5.3. Comparative Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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PSADWT | Proposed System Architecture Discrete Wavelet Transform |

ND | Noise Detection |

IR | Image Restoration |

PDF | Probability Density Function |

ANN | Artificial Neural Network |

RDNPSNR | Residual Dense Network Peak signal-to-noise ratio |

IO | Ideal Observer |

AMT | Automatic Machine Translation |

DWT | Discrete Wavelet Transform |

FDM | Frame Difference Method |

AIWTRCNN | Artificial Intelligence Wavelet Transform Region-based Convolutional Neural Network |

Wavelet Level | db Value | Accuracy (%) |
---|---|---|

2 | 1 | 89.23 |

2 | 2 | 92.34 |

2 | 3 | 95.23 |

2 | 4 | 93.12 |

2 | 5 | 92.59 |

3 | 1 | 94.93 |

3 | 2 | 92.34 |

3 | 3 | 95.23 |

3 | 4 | 97.23 |

3 | 5 | 98.93 |

4 | 1 | 96.34 |

4 | 2 | 95.23 |

4 | 3 | 97.52 |

4 | 4 | 98.29 |

4 | 5 | 97.42 |

Class Name | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|

Poisson noise | 98.75% | 97% | 98% | 98% |

Speckle noise | 99.50% | 99% | 99% | 100% |

Gaussian noise | 99% | 98% | 98% | 97% |

Impulse value noise | 99.75% | 100% | 99% | 99% |

Algorithm | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|

PSA | 99.25% | 98.50% | 98.50% | 98.50% |

AlexNet | 97.30% | 96.60% | 95.70% | 97.93% |

Yolo V5 | 94.09% | 94.58% | 93.10% | 94% |

Yolo V3 | 92.24% | 90.88% | 91.20% | 93.17% |

RCNN | 90.03% | 92.08% | 90.39% | 92.13% |

CNN | 89.39% | 89.53% | 88.62% | 88.82% |

Number of Hidden Layers | Accuracy |
---|---|

1 | 86.23 |

2 | 87.43 |

3 | 89.56 |

4 | 90.88 |

5 | 92.66 |

6 | 94.09 |

7 | 95.69 |

8 | 97.29 |

9 | 98.93 |

10 | 97.29 |

11 | 96.23 |

Algorithm | Class Name | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|

PSA | Gaussian noise | 99.00% | 98.00% | 98.00% | 98.00% |

Impulse value noise | 99.75% | 100% | 99.00% | 100% | |

Poisson noise | 98.75% | 97.00% | 98.00% | 97.00% | |

Speckle noise | 99.50% | 99.00% | 99.00% | 99.00% | |

AlexNet | Gaussian noise | 98.62% | 97.13% | 96.42% | 98.62% |

Impulse value noise | 97.41% | 96.30% | 95.23% | 98.17% | |

Poisson noise | 97.32% | 95.14% | 93.62% | 96.42% | |

Speckle noise | 96.21% | 97.83% | 97.53% | 98.50% | |

Yolo V5 | Gaussian noise | 96.43% | 94.72% | 93.84% | 94.24% |

Impulse value noise | 95.72% | 94.28% | 93.63% | 95.24% | |

Poisson noise | 92.52% | 96.46% | 91.30% | 92.88% | |

Speckle noise | 91.69% | 92.84% | 93.61% | 93.62% | |

Yolo V3 | Gaussian noise | 95.24% | 92.43% | 94.20% | 93.53% |

Impulse value noise | 89.63% | 90.75% | 91.64% | 94.45% | |

Poisson noise | 92.48% | 91.30% | 87.53% | 92.12% | |

Speckle noise | 91.59% | 89.03% | 91.41% | 92.60% | |

RCNN | Gaussian noise | 86.43% | 89.35% | 87.53% | 90.70% |

Impulse value noise | 89.53% | 92.54% | 90.51% | 91.73% | |

Poisson noise | 93.15% | 94.02% | 92.09% | 92.56% | |

Speckle noise | 91.00% | 92.42% | 91.42% | 93.51% | |

CNN | Gaussian noise | 87.35% | 86.25% | 84.62% | 85.73% |

Impulse value noise | 88.53% | 87.53% | 86.83% | 87.62% | |

Poisson noise | 95.42% | 96.42% | 96.00% | 96.19% | |

Speckle noise | 86.24% | 87.92% | 87.03% | 85.72% |

PSA | ||
---|---|---|

Image Size | Time (Seconds) | Required Memory for Processing (kb) |

50 kb | 0.0001 | 12 |

100 kb | 0.0001 | 16 |

200 kb | 0.000294 | 25 |

500 kb | 0.000784 | 39 |

750 kb | 0.001862 | 48 |

1 Mb | 0.1274 | 74 |

5 Mb | 1.0388 | 91 |

10 Mb | 2.764 | 128 |

15 Mb | 2.9543 | 381 |

Configuration | |||
---|---|---|---|

CPU/GPU | Processor | RAM | Required Time in Seconds |

CPU | i3 | 8GB | 0.393 |

CPU | i5 | 8GB | 0.292 |

CPU | i7 | 8GB | 0.286 |

GPU | Nvidia K80 | 24 GB | 0.003 |

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## Share and Cite

**MDPI and ACS Style**

Pawar, P.; Ainapure, B.; Rashid, M.; Ahmad, N.; Alotaibi, A.; Alshamrani, S.S.
Deep Learning Approach for the Detection of Noise Type in Ancient Images. *Sustainability* **2022**, *14*, 11786.
https://doi.org/10.3390/su141811786

**AMA Style**

Pawar P, Ainapure B, Rashid M, Ahmad N, Alotaibi A, Alshamrani SS.
Deep Learning Approach for the Detection of Noise Type in Ancient Images. *Sustainability*. 2022; 14(18):11786.
https://doi.org/10.3390/su141811786

**Chicago/Turabian Style**

Pawar, Poonam, Bharati Ainapure, Mamoon Rashid, Nazir Ahmad, Aziz Alotaibi, and Sultan S. Alshamrani.
2022. "Deep Learning Approach for the Detection of Noise Type in Ancient Images" *Sustainability* 14, no. 18: 11786.
https://doi.org/10.3390/su141811786