A Residual Dense U-Net Neural Network for Image Denoising

Gurrola-Ramos, Javier; Dalmau, Oscar; Alarcón, Teresa E.

doi:10.1109/access.2021.3061062

Cited by 93 publications

(44 citation statements)

References 34 publications

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“…In addition, it uses the skip connection between encoders and decoders to enhance the reconstruction process of images. UNet is widely used in many computer vision tasks like segmentation, restoration [9], [26]. Furthermore, it has various improved versions like Res-UNet [27], Dense-UNet [28], Attention UNet [29] and Non-local UNet [30].…”

Section: B Unetmentioning

confidence: 99%

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SUNet: Swin Transformer UNet for Image Denoising

Fan¹,

Liu²,

Liu³

2022

Preprint

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Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost dominated the computer vision and had achieved considerable success in different levels of vision tasks including image restoration. However, recently the Swin Transformer-based model also shows impressive performance, even surpasses the CNN-based methods to become the state-of-the-art on high-level vision tasks. In this paper, we proposed a restoration model called SUNet which uses the Swin Transformer layer as our basic block and then is applied to UNet architecture for image denoising. The source code and pre-trained models are available at https://github.com/FanChiMao/SUNet.

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Section: B Unetmentioning

confidence: 99%

“…Training Set. Using the same experimental setups of image denoising [8], [9], we train our model on image superresolution DIV2K [10] dataset which has 800 and 100 highquality (the average resolution is about 1920 × 1080) images [36] AND KODAK24 DATASET [37]…”

Section: B Experiments Datasetsmentioning

confidence: 99%

SUNet: Swin Transformer UNet for Image Denoising

Fan¹,

Liu²,

Liu³

2022

Preprint

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“…VGG-16 and Inception-v3 were used for the classification of the noised image while a CNN-based denoising method FFDNet was used to denoise noise. J. Gurrola-Ramos [7] purposed a residual Dense U-Net Neural Network to the denoise image. The purposed model has many features like the denoising process does not need knowledge regarding noise before denoising.…”

Section: Literature Reviewmentioning

confidence: 99%

“…MSE stands for mean square error. Its mathematical representation is shown in (7). The m*n represents noisefree monochrome image 'I' having 'K' as noise approximation.…”

Section: Generation Of Noisy Imagesmentioning

confidence: 99%

Facial image noise classification and denoising using neural network

Tripathi

2021

Sustainable Engineering and Innovation

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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.

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“…O. Sheremet [8] purposed a CNN-based model for denoising images in Info communication systems. J. Gurrola-Ramos, [9] purpose a dense U-Net neural network. To denoise the picture, a residual Dense U-Net Neural Network was used.…”

Section: Introductionmentioning

confidence: 99%

Facial image denoising using AutoEncoder and UNET

Tripathi

2021

Heritage and Sustainable Development

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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.

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A Residual Dense U-Net Neural Network for Image Denoising

Cited by 93 publications

References 34 publications

SUNet: Swin Transformer UNet for Image Denoising

SUNet: Swin Transformer UNet for Image Denoising

Facial image noise classification and denoising using neural network

Facial image denoising using AutoEncoder and UNET

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