scholarly journals Facial image noise classification and denoising using neural network

2021 ◽  
Vol 3 (2) ◽  
pp. 102-111
Author(s):  
Milan Tripathi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Denoising ◽  
Image Noise ◽  
Facial Image ◽  
Noisy Images ◽  
Image Dataset ◽  
Noise Classification

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.

scholarly journals Facial image denoising using AutoEncoder and UNET

2021 ◽  
Vol 3 (2) ◽  
pp. 89-96
Author(s):  
Milan Tripathi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Image Quality ◽  
Image Denoising ◽  
Network Architecture ◽  
Multiple Model ◽  
Facial Image ◽  
Neural Network Architecture ◽  
Noisy Images

Image denoising is a crucial topic in image processing. Noisy images are generated due to technical and environmental errors. Therefore, it is reasonable to consider image denoising an important topic to study, as it also helps to resolve other image processing issues. However, the challenge is that the classical techniques used are time-consuming and not flexible enough. This article compares the two major neural network architecture which looks promising to resolve this issues. The AutoEncoder and UNET is now the most researched subject in deep learning for image denoising. Multiple model architectures are designed, implement, and evaluated. The dataset is preprocessed and then it is used to train and test the model. It is clearly shown in this paper which model performs the best in this task by comparing both models using the most used parameters to evaluate image quality PSNR and SSIM.

scholarly journals Implementasi Kombinasi Metode Mean Denoising dan Convolutional Neural Network pada Facial Landmark Detection

2021 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
I Putu Agus Eka Darma Udayana ◽  
I Kadek Dwi Gandika Supartha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Facial Image ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmark ◽  
Image Dataset

Facial landmark detectionmerupakan bagian dari facial recognition,bertujuan untuk mengidentifikasi titik fokus pada wajah berdasarkan ciri penampakan bagian wajah yang cenderung menonjol, seperti area mata, hidung, bibir, serta tulang pipi. Facial landmark detection sering diimplementasikan pada bidang pengenalan wajah, prediksi pose wajah, rekonstruksi wajah 3 dimensi, serta pengembangan sistem deteksi kelelahan karyawan berdasarkan ekspresi wajah. Seiring bertambahnya ketersediaan citra wajah dan kebutuhan proses komputasi yang cepat, metode Convolutional Neural Network (CNN) diimplementasikan pada facial landmark detection. Namun beragamnya kualitas citra menyebabkan CNN kurang optimal dalam melakukan deteksi. Oleh karena itu guna mengatasi permasalahan terkait kualitas citra ini, diimplementasikan metode mean denoising sebagai upaya peningkatan nilai akurasi CNN dalam melakukan pendeteksian landmark wajah. Dataset citra wajah diperoleh dari platform Kaggle, LFW-People, AFLW200 dan Female Facial Image Dataset, dengan total sebanyak 2.050 citra wajah, dan terbagi menjadi 2.000 data latih dan 50 data uji. Berdasarkan hasil pengujian, kombinasi metode CNN dengan mean denoising menghasilkan peningkatan akurasi yang lebih baik dalam pengenalan objek pada wajah pada kualitas citra yang heterogen dengan rata-rata akurasi pengujian sebesar 81,33%.Akurasi yang cukup baik ini didapatkan karena citra wajah masukan dilakukan penghilangan noise terlebih dahulu sehingga fitur dari citra yang seringkali menyebabkan sistem CNN salah dalam mengidentifikasi objek pada wajah dapat diminimalisir.

scholarly journals Fully Symmetric Convolutional Network for Effective Image Denoising

2019 ◽  
Vol 9 (4) ◽  
pp. 778 ◽  
Author(s):  
Steffi Priyanka ◽  
Yuan-Kai Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Denoising ◽  
Gpu Computing ◽  
State Of The Art ◽  
Large Data ◽  
Convolutional Network ◽  
Proposed Model ◽  
Feature Extractor ◽  
A Chain

Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional–deconvolutional layers. This framework learns convolutional–deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstractions to recover the image content details. An adaptive moment optimizer is used to minimize the reconstruction loss as it is appropriate for large data and noisy images. Extensive experiments were conducted for image denoising to evaluate the FSCN model against the existing state-of-the-art denoising algorithms. The results show that the proposed model achieves superior denoising, both qualitatively and quantitatively. This work also presents the efficient implementation of the FSCN model by using GPU computing which makes it easy and attractive for practical denoising applications.

scholarly journals Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2998
Author(s):  
Aamir Khan ◽  
Weidong Jin ◽  
Amir Haider ◽  
MuhibUr Rahman ◽  
Desheng Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Computer Vision ◽  
Image Denoising ◽  
Theoretical Study ◽  
State Of The Art ◽  
Multiple Level ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Adversarial Network

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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