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 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 High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1370 ◽  
Author(s):  
Tingzhu Sun ◽  
Weidong Fang ◽  
Wei Chen ◽  
Yanxin Yao ◽  
Fangming Bi ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Pedestrian Detection ◽  
Image Inpainting ◽  
Resolution Image ◽  
Multi Scale ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Generation Network ◽  
High Resolution Images

Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene.

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 Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jingjing Zhao ◽  
Yongxiang Liu ◽  
Kai Huo ◽  
Jiaxi Ye ◽  
Bo Xiao
Keyword(s):  
High Resolution ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Resolution Image ◽  
High Resolution Image ◽  
Ofdm Modulation

Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.

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 Stacked generative adversarial networks for image compositing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bing Yu ◽  
Youdong Ding ◽  
Zhifeng Xie ◽  
Dongjin Huang
Keyword(s):  
High Resolution ◽  
Visual Performance ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Generative Adversarial Network ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Image Compositing ◽  
Efficiency And Effectiveness

AbstractPerfect image compositing can harmonize the appearance between the foreground and background effectively so that the composite result looks seamless and natural. However, the traditional convolutional neural network (CNN)-based methods often fail to yield highly realistic composite results due to overdependence on scene parsing while ignoring the coherence of semantic and structural between foreground and background. In this paper, we propose a framework to solve this problem by training a stacked generative adversarial network with attention guidance, which can efficiently create a high-resolution, realistic-looking composite. To this end, we develop a diverse adversarial loss in addition to perceptual and guidance loss to train the proposed generative network. Moreover, we construct a multi-scenario dataset for high-resolution image compositing, which contains high-quality images with different styles and object masks. Experiments on the synthesized and real images demonstrate the efficiency and effectiveness of our network in producing seamless, natural, and realistic results. Ablation studies show that our proposed network can improve the visual performance of composite results compared with the application of existing methods.

scholarly journals Artificial Intelligence-Assisted Fresco Restoration with Multiscale Line Drawing Generation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guanghui Song ◽  
Hai Wang
Keyword(s):  
Artificial Intelligence ◽  
Network Structure ◽  
Signal To Noise Ratio ◽  
Line Drawing ◽  
Structural Similarity ◽  
Block Matching ◽  
Colour Space ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Hsv Colour Space

In this article, we study the mural restoration work based on artificial intelligence-assisted multiscale trace generation. Firstly, we convert the fresco images to colour space to obtain the luminance and chromaticity component images; then we process each component image to enhance the edges of the exfoliated region using high and low hat operations; then we construct a multistructure morphological filter to smooth the noise of the image. Finally, the fused mask image is fused with the original mural to obtain the final calibration result. The fresco is converted to HSV colour space, and chromaticity, saturation, and luminance features are introduced; then the confidence term and data term are used to determine the priority of shedding boundary points; then a new block matching criterion is defined, and the best matching block is obtained to replace the block to be repaired based on the structural similarity between the block to be repaired and the matching block by global search; finally, the restoration result is converted to RGB colour space to obtain the final restoration result. An improved generative adversarial network structure is proposed to address the shortcomings of the existing network structure in mural defect restoration, and the effectiveness of the improved modules of the network is verified. Compared with the existing mural restoration algorithms on the test data experimentally verified, the peak signal-to-noise ratio (PSNR) score is improved by 4% and the structural similarity (SSIM) score is improved by 2%.

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