Night-to-Day Road Scene Translation Using Generative Adversarial Network with Structural Similarity Loss for Night Driving Safety

2021 ◽  
pp. 119-133
Author(s):  
Igi Ardiyanto ◽  
Indah Soesanti ◽  
Dwiyan Cahya Qairawan
Keyword(s):  
Structural Similarity ◽  
Driving Safety ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

scholarly journals Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

2020 ◽  
Vol 10 (1) ◽  
pp. 375 ◽  
Author(s):  
Zetao Jiang ◽  
Yongsong Huang ◽  
Lirui Hu
Keyword(s):  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Features ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Adversarial Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To further enhance the visual quality, we propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN). A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the total amount of parameters. For the discriminator network, the batch normalization (BN) layer was discarded, and the problem of artifacts was reduced. A frequency energy similarity loss function was designed to constrain the generator network to generate better super-resolution images. Experiments on several different datasets showed that the peak signal-to-noise ratio (PSNR) was improved by more than 3 dB, structural similarity index (SSIM) was increased by 16%, and the total parameter was reduced to 42.8% compared with the original model. Combining various objective indicators and subjective visual evaluation, the algorithm was shown to generate richer image details, clearer texture, and lower complexity.

scholarly journals Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianfang Cao ◽  
Zibang Zhang ◽  
Aidi Zhao
Keyword(s):  
High Resolution ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Image Feature ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Set Up

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.

scholarly journals Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

2019 ◽  
Vol 9 (11) ◽  
pp. 2358 ◽  
Author(s):  
Minsoo Hong ◽  
Yoonsik Choe
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Structural Similarity ◽  
Wasserstein Distance ◽  
Perceptual Similarity ◽  
Generative Adversarial Network ◽  
Edge Information ◽  
Adversarial Network ◽  
Blurred Images ◽  
Parameter Values

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network methods, such as the generative adversarial network (GAN), which is a powerful generative network. Among many different types of GAN, the proposed method is performed using the Wasserstein generative adversarial network with gradient penalty (WGANGP). Since edge information is the most important factor in an image, the style loss function is applied to represent the perceptual information of the edge in order to preserve small edge information and capture its perceptual similarity. As a result, the proposed method improves the similarity between sharp and blurred images by minimizing the Wasserstein distance, and it captures well the perceptual similarity using the style loss function, considering the correlation of features in the convolutional neural network (CNN). To confirm the performance of the proposed method, three experiments are conducted using two datasets: the GOPRO Large and Kohler dataset. The optimal solutions are found by changing the parameter values experimentally. Consequently, the experiments depict that the proposed method achieves 0.98 higher performance in structural similarity (SSIM) and outperforms other de-blurring methods in the case of both datasets.

scholarly journals QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tae-Hoon Yong ◽  
Su Yang ◽  
Sang-Jeong Lee ◽  
Chansoo Park ◽  
Jo-Eun Kim ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Cone Beam Ct ◽  
Structural Similarity ◽  
Training Data ◽  
Cone Beam ◽  
Absolute Difference ◽  
Mineral Density ◽  
Quantitative Ct ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbstractThe purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

scholarly journals Label2label: Training a neural network to selectively restore cellular structures in fluorescence microscopy

2022 ◽  
Author(s):  
Lisa Sophie Kölln ◽  
Omar Salem ◽  
Jessica Valli ◽  
Carsten Gram Hansen ◽  
Gail McConnell
Keyword(s):  
Neural Network ◽  
Cellular Structure ◽  
Structural Similarity ◽  
Training Data ◽  
Cellular Structures ◽  
Target Structure ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Adversarial Network ◽  
Image Pairs

Immunofluorescence (IF) microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in IF microscopy, but current methods cannot correct for those background signals. We report a new method that trains a CNN to reduce unspecific signals in IF images; we name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure. We show that after L2L training a network predicts images with significantly increased contrast of a target structure, which is further improved after implementing a multi-scale structural similarity loss function. Here, our results suggest that sample differences in the training data decrease hallucination effects that are observed with other methods. We further assess the performance of a cycle generative adversarial network, and show that a CNN can be trained to separate structures in superposed IF images of two targets.

scholarly journals An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shengnan Zhang ◽  
Lei Wang ◽  
Chunhong Chang ◽  
Cong Liu ◽  
Longbo Zhang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Evaluation Criteria ◽  
Structural Similarity ◽  
Block Matching ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Edge Preserving ◽  
Block Matching Algorithm ◽  
Adversarial Network

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.

scholarly journals An Enhanced pix2pix Dehazing Network with Guided Filter Layer

2020 ◽  
Vol 10 (17) ◽  
pp. 5898
Author(s):  
Qirong Bu ◽  
Jie Luo ◽  
Kuan Ma ◽  
Hongwei Feng ◽  
Jun Feng
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Comparison Method ◽  
Guided Filter ◽  
Generative Adversarial Network ◽  
Test Dataset ◽  
Adversarial Network ◽  
Filter Layer

In this paper, we propose an enhanced pix2pix dehazing network, which generates clear images without relying on a physical scattering model. This network is a generative adversarial network (GAN) which combines multiple guided filter layers. First, the input of hazy images is smoothed to obtain high-frequency features according to different smoothing kernels of the guided filter layer. Then, these features are embedded in higher dimensions of the network and connected with the output of the generator’s encoder. Finally, Visual Geometry Group (VGG) features are introduced to serve as a loss function to improve the quality of the texture information restoration and generate better hazy-free images. We conduct experiments on NYU-Depth, I-HAZE and O-HAZE datasets. The enhanced pix2pix dehazing network we propose produces increases of 1.22 dB in the Peak Signal-to-Noise Ratio (PSNR) and 0.01 in the Structural Similarity Index Metric (SSIM) compared with a second successful comparison method using the indoor test dataset. Extensive experiments demonstrate that the proposed method has good performance for image dehazing.

scholarly journals Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhihua Li ◽  
Weili Shi ◽  
Qiwei Xing ◽  
Yu Miao ◽  
Wei He ◽  
...  
Keyword(s):  
Loss Function ◽  
Low Dose ◽  
Structural Similarity ◽  
Image Method ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Quantitative Measurements ◽  
Better Than

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

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