Improved BM3D image denoising using SSIM-optimized Wiener filter

Hasan, Mahmud; El-Sakka, Mahmoud R.

doi:10.1186/s13640-018-0264-z

Cited by 62 publications

(24 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PSRN values of MP-CNN model are all larger than WNNM, EPLL, TNRD, DnCNN-S and SCNN models, indicating that MP-CNN had the best effect on noise removal in images. In a study showed that SIMM value was used to improve the filter and found that noise in the image could be removed better [8]. Some researchers used PSNR, MSE and SSIM to carry out a qualitative and quantitative comparison of image indicators [9].…”

Section: Discussionmentioning

confidence: 99%

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

Long¹,

Thomas²

2020

Malays. sport. j.

View full text Add to dashboard Cite

To explore the effect of applying multi-layer convolutional neural network to noise removal during the acquisition and transmission of functional motion images, the classical image denoising algorithms of mean filtering, median filtering, and wavelet transform filtering are first introduced. In addition, the evaluation methods of mean square error (MSE), image enhancement factor (IEF), peak signal to noise ratio (PSNR) and structural similarity measure (SSIM) in the image quality evaluation index system are introduced. Based on the convolutional neural network model, a multi-scale parallel convolutional neural network (MP-CNN) model is constructed to remove the noise in the image, and the functional action image is devoted to different degrees. Finally, the denoising effect is evaluated by the objective and subjective evaluation system. The objective evaluation results show that MP-CNN's MSE, IEF, PSNR, and SSIM are better than the single-channel model, and the test time is shorter. The subjective evaluation results show that the MP-CNN model has the best effect on noise removal after 25 denoising of functional action images. In this study, a multi-channel image denoising model based on the multi-layer convolutional neural network can improve the effect of functional motion image noise removal. KEYWORDSMulti-layer convolutional neural network, Functional action-image, Noise, Evaluation system METHODOLOGY Image denoising algorithmNoise generally has the characteristics of random distribution, and the probability density function can be used to describe the noise distribution, which can be divided into Gaussian, Rayleigh, exponent, γ, uniform and impulse noise and so on. The classical denoising methods include mean filtering, median filtering and wavelet transform filtering. Mean filtering is a common spatial linear filtering method, which plays an obvious role in smoothing noise, and the Box template is often used to filter noise images.

show abstract

Section: Discussionmentioning

confidence: 99%

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

Long¹,

Thomas²

2020

Malays. sport. j.

View full text Add to dashboard Cite

show abstract

“… Removing the noise Noise reduction is an essential step in image processing due to its importance in obtaining better results, the block matching with three-dimensional filtering (BM3D) denoising is used in this step. Many studies shown that the BM3D filter is very important in term of noise reduction due to its results comparing with other filters [9,10]. The implementation of this filter is explained in detail in [11]  Contrast…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

Offline signatures matching using haar wavelet subbands

2020

View full text Add to dashboard Cite

The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems to satisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works tried to develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different sets of features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used as a dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition.

show abstract

“…After the development of the best analytical solution, BM3D [8], [18], little improvement in denoising performance was achieved until the advent of deep learning denoisers [59]. Recent Convolutional Neural Network (CNN) based methods achieve state-of-the-art image denoising performance and are even faster than traditional optimizationbased approaches [55].…”

Section: Introductionmentioning

confidence: 99%

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Helou

Süsstrunk

2020

IEEE Trans. on Image Process.

106

View full text Add to dashboard Cite

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network's generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of 0.1d B, whether trained on or not.

show abstract

Improved BM3D image denoising using SSIM-optimized Wiener filter

Cited by 62 publications

References 21 publications

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

Offline signatures matching using haar wavelet subbands

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Contact Info

Product

Resources

About