A neural network based low-light image denoising method

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Download Full-text

Image denoising method based on a deep convolution neural network

IET Image Processing ◽

10.1049/iet-ipr.2017.0389 ◽

2018 ◽

Vol 12 (4) ◽

pp. 485-493 ◽

Cited By ~ 25

Author(s):

Fu Zhang ◽

Nian Cai ◽

Jixiu Wu ◽

Guandong Cen ◽

Han Wang ◽

...

Keyword(s):

Neural Network ◽

Image Denoising ◽

Convolution Neural Network ◽

Denoising Method ◽

Deep Convolution Neural Network

Download Full-text

A Novel Approach of Low-Light Image Denoising for Face Recognition

Advances in Mechanical Engineering ◽

10.1155/2014/256790 ◽

2014 ◽

Vol 6 ◽

pp. 256790

Author(s):

Yimei Kang ◽

Wang Pan

Keyword(s):

Face Recognition ◽

Image Denoising ◽

Recognition Rate ◽

Low Frequency ◽

Frequency Noise ◽

Low Frequency Noise ◽

Face Database ◽

Low Light ◽

Very Low Frequency ◽

Light Image

Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE) to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG) to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis) recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.

Download Full-text

A Pipeline Neural Network for Low-Light Image Enhancement

IEEE Access ◽

10.1109/access.2019.2891957 ◽

2019 ◽

Vol 7 ◽

pp. 13737-13744 ◽

Cited By ~ 9

Author(s):

Yanhui Guo ◽

Xue Ke ◽

Jie Ma ◽

Jun Zhang

Keyword(s):

Neural Network ◽

Image Enhancement ◽

Low Light ◽

Light Image

Download Full-text

Improvement of Low-Light Image by Convolutional Neural Network

2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas.2019.8885098 ◽

2019 ◽

Cited By ~ 1

Author(s):

Manbae Kim

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Low Light ◽

Light Image

Download Full-text

Variance Reduction in Low Light Image Enhancement Model

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4723.119420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 139-142

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Image Enhancement ◽

Variance Reduction ◽

Low Light ◽

Light Image ◽

Hidden Layer

In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer is added to the model which results in a decrease in irregularity.

Download Full-text

Low Light Image Enhancement using Convolutional Neural Network

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35787 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3463-3472

Author(s):

Choundur Vishnu

Keyword(s):

Neural Network ◽

Image Quality ◽

Convolutional Neural Network ◽

Image Enhancement ◽

Scene Understanding ◽

Object Location ◽

Low Light ◽

Light Image ◽

Image Improvement

Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for object location and scene understanding. In some cases there is a condition when image caught in low light consistently experience the ill effects of low difference and splendor which builds the trouble of resulting undeniable level undertaking in incredible degree. Low light image improvement utilizing convolutional neural network framework accepts dull or dark images as information and creates brilliant images as a yield without upsetting the substance of the image. So understanding the scene caught through image becomes simpler task.

Download Full-text

An Image Denoising Method Based on BP Neural Network Optimized by Improved Whale Optimization Algorithm

10.21203/rs.3.rs-137201/v1 ◽

2021 ◽

Author(s):

Chunzhi Wang ◽

Min Li ◽

Ruoxi Wang ◽

Han Yu ◽

Shuping Wang

Keyword(s):

Neural Network ◽

Image Denoising ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Weight Coefficient ◽

Whale Optimization Algorithm ◽

Denoising Method ◽

Training Time ◽

Whale Optimization ◽

City Construction

Abstract As an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model, and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed algorithm has better performance in peak signal-to-noise ratio (PSNR) than other algorithms.

Download Full-text