Flashlight CNN Image Denoising

Binh, Pham Huu Thanh; Cruz, Cristóvão; Егиазарян, Карен

doi:10.23919/eusipco47968.2020.9287793

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Flashlight CNNs are another type of convolutional neural network implementing deep NN for noise removal processes. The main structure of this method is based on the combination of deep residual and inception networks [12]. Utilizing inception layers provides us with overcoming and addressing the reuse of diminishing features while tackling additive white gaussian noise.…”

Section: B Flashlight Cnn (Flcnn)mentioning

confidence: 99%

“…As shown in Fig. 3, this network consists of two main phases [12]:  Warmup phase which utilizes convolutional layers (typical or conventional CNN). There are two main stages in this phase.…”

Section: B Flashlight Cnn (Flcnn)mentioning

confidence: 99%

“…There are also some other issues which deep neural network-based methods are suffering from. One is diminishing feature reuse, and the other is that increasing the number of parameters and layers does not have any advantage for them [12]. To address these issues Bin et al developed a flashlight CNN method based on deep residual and inception networks that is able to hold many parameters [12].…”

Section: Introductionmentioning

confidence: 99%

“…One is diminishing feature reuse, and the other is that increasing the number of parameters and layers does not have any advantage for them [12]. To address these issues Bin et al developed a flashlight CNN method based on deep residual and inception networks that is able to hold many parameters [12]. Additionally, J. Zhang in [11] developed a diamond denoiser to deal with the issue of losing network's gradient caused by deeper networks.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network-based Methods for Brain Image De-noising: A Short Comparison

Rahimi,

Golilarz

2024

IJACSA

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Various types of noise may affect the visual quality of images during capturing and transmitting procedures. Finding a proper technique to remove the possible noise and improve both quantitative and qualitative results is always considered as one of the most important and challenging pre -processing tasks in image and signal processing. In this paper, we made a short comparison between two well-known approaches called thresholding neural network (TNN) and deep neural network (DNN) based methods for image de-noising. De-noising results of TNNs, Dn-CNNs, Flashlight CNN (FLCNN) and Diamond denoising networks (DmDN) have been compared with each other. In this regard, several experiments have been performed in terms of Peak S ignal to Noise Ratio (PS NR) to validate the performance analysis of various de-noising methods. The analysis indicates that DmDNs perform better than other learning-based algorithms for de -noising brain MR images. DmDN achieved a PS NR value of 29.85 dB, 30.74 dB, 29.15 dB, and 29.45 dB for denoising MR image 1, MR image 2, MR image 3 and MR Image 4, respectively for a standard deviation of 15.

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Section: B Flashlight Cnn (Flcnn)mentioning

confidence: 99%

Section: B Flashlight Cnn (Flcnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network-based Methods for Brain Image De-noising: A Short Comparison

Rahimi,

Golilarz

2024

IJACSA

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“…In addition, Zhang et al [18] further improved the DnCNN and proposed a fast and flexible denoising convolutional neural network (FFDNet), which achieves a good trade-off between the inference speed and denoising performance by downsampling and manually inputting a noise estimation map. Binh et al [23] combined DnCNN with ResNet and proposed a convolutional denoising neural network called FlashLight CNN. A complex-valued deep convolutional neural network called CDNet was proposed by Quan et al [24], and it effectively improved the denoising performance of the network.…”

Section: Introductionmentioning

confidence: 99%

NSNet: An N-Shaped Convolutional Neural Network with Multi-Scale Information for Image Denoising

Chen

2023

Mathematics

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Deep learning models with convolutional operators have received widespread attention for their good image denoising performance. However, since the convolutional operation prefers to extract local features, the extracted features may lose some global information, such as texture, structure, and color characteristics, when the object in the image is large. To address this issue, this paper proposes an N-shaped convolutional neural network with the ability to extract multi-scale features to capture more useful information and alleviate the problem of global information loss. The proposed network has two main parts: a multi-scale input layer and a multi-scale feature extraction layer. The former uses a two-dimensional Haar wavelet to create an image pyramid, which contains the corrupted image’s high- and low-frequency components at different scales. The latter uses a U-shaped convolutional network to extract features at different scales from this image pyramid. The method sets the mean-squared error as the loss function and uses the residual learning strategy to learn the image noise directly. Compared with some existing image denoising methods, the proposed method shows good performance in gray and color image denoising, especially in textures and contours.

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