scholarly journals Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3387 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Image Generation ◽  
Convolutional Network ◽  
Biomedical Image ◽  
Reflection Data ◽  
Network Connection ◽  
Reflection Image ◽  
Heterogeneous Source

In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.

scholarly journals Color Image Generation from Range and Reflection Data of LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5414
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Point Clouds ◽  
Range Data ◽  
Fusion Method ◽  
Image Generation ◽  
Learning Networks ◽  
Reflection Data

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

scholarly journals Blind Color Image Watermarking Using Fan Beam Transform and QR Decomposition

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 486
Author(s):  
Pranab Kumar Dhar ◽  
Pulak Hazra ◽  
Tetsuya Shimamura
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Triangular Matrix ◽  
Structural Similarity ◽  
Qr Decomposition ◽  
Original Image ◽  
Color Image Watermarking ◽  
Multimedia Contents

Digital watermarking has been utilized effectively for copyright protection of multimedia contents. This paper suggests a blind symmetric watermarking algorithm using fan beam transform (FBT) and QR decomposition (QRD) for color images. At first, the original image is transferred from RGB to L*a*b* color model and FBT is applied to b* component. Then the b*component of the original image is split into m × m non-overlapping blocks and QRD is conducted to each block. Watermark data is placed into the selected coefficient of the upper triangular matrix using a new embedding function. Simulation results suggest that the presented algorithm is extremely robust against numerous attacks, and also yields watermarked images with high quality. Furthermore, it represents more excellent performance compared with the recent state-of-the-art algorithms for robustness and imperceptibility. The normalized correlation (NC) of the proposed algorithm varies from 0.8252 to 1, the peak signal-to-noise ratio (PSNR) varies from 54.1854 to 54.1892, and structural similarity (SSIM) varies from 0.9285 to 0.9696, respectively. In contrast, the NC of the recent state-of-the-art algorithms varies from 0.5193 to 1, PSNR varies from 38.5471 to 52.64, and SSIM varies from 0.9311 to 0.9663, respectively.

scholarly journals Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4818 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ju H. Park ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Ground Truth ◽  
Sensor Data ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network ◽  
Ground Truth Image ◽  
And Performance ◽  
Reflection Intensity

In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from the projected intensity by using a fully convolutional network (FCN). The color image should be generated from a very sparse projected intensity image. For this reason, the FCN is designed to have an asymmetric network structure, i.e., the layer depth of the decoder in the FCN is deeper than that of the encoder. The well-known KITTI dataset for various scenarios is used for the proposed FCN training and performance evaluation. Performance of the asymmetric network structures are empirically analyzed for various depth combinations for the encoder and decoder. Through simulations, it is shown that the proposed method generates fairly good visual quality of images while maintaining almost the same color as the ground truth image. Moreover, the proposed FCN has much higher performance than conventional interpolation methods and generative adversarial network based Pix2Pix. One interesting result is that the proposed FCN produces shadow-free and daylight color images. This result is caused by the fact that the LiDAR sensor data is produced by the light reflection and is, therefore, not affected by sunlight and shadow.

scholarly journals Convolution Kernel Operations on a Two-Dimensional Spin Memristor Cross Array

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6229
Author(s):  
Saike Zhu ◽  
Lidan Wang ◽  
Zhekang Dong ◽  
Shukai Duan
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Computing System ◽  
Edge Extraction ◽  
Data Intensive ◽  
New Type ◽  
Processing Effects ◽  
Memory Bottleneck ◽  
Time And Energy

In recent years, convolution operations often consume a lot of time and energy in deep learning algorithms, and convolution is usually used to remove noise or extract the edges of an image. However, under data-intensive conditions, frequent operations of the above algorithms will cause a significant memory/communication burden to the computing system. This paper proposes a circuit based on spin memristor cross array to solve the problems mentioned above. First, a logic switch based on spin memristors is proposed, which realizes the control of the memristor cross array. Secondly, a new type of spin memristor cross array and peripheral circuits is proposed, which realizes the multiplication and addition operation in the convolution operation and significantly alleviates the computational memory bottleneck. At last, the color image filtering and edge extraction simulation are carried out. By calculating the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image result, the processing effects of different operators are compared, and the correctness of the circuit is verified.

scholarly journals A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 975 ◽  
Author(s):  
Shaekh Hasan Shoron ◽  
Monamy Islam ◽  
Jia Uddin ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
...  
Keyword(s):  
Digital Watermarking ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Threshold Value ◽  
Discrete Wavelet ◽  
Biomedical Images ◽  
Fuzzy C Means ◽  
Biomedical Image ◽  
Watermarking Scheme

Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).

scholarly journals Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
N Yousefi Moteghaed
Keyword(s):  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Speckle Noise ◽  
Ct Images ◽  
Biomedical Image ◽  
Processing Step ◽  
Salt And Pepper ◽  
Medical Analysis

Abstract Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.

Secure Image Watermarking Based on LWT and SVD

2018 ◽  
Vol 18 (04) ◽  
pp. 1850021 ◽  
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Speech Signal ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Structural Similarity ◽  
Signal Transformation ◽  
Host Image ◽  
Confidential Data ◽  
Value Decomposition ◽  
Watermarking Scheme

Nowadays, digital watermarking is employed for authentication and copyright protection. In this paper, a secure image watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD), is proposed. Both LWT and SVD are used as mathematical tools for embedding watermark in the host image. In this work, the watermark is a speech signal which is segmented into shorted portions having the same length. This length is equal to 256 and these different portions constitute the different columns of a speech image. The latter is then embedded into a grayscale or color image (the host image). This procedure is performed in order to insert into an image a confidential data which is in our case a speech signal. But instead of embedding this speech signal directly into the image, we transform it into a matrix and treated it as an image (“a speech image”). Of course, this speech signal transformation permits us to use LWT-2D and SVD to both the host image and the watermark (“a speech image”). The proposed technique is applied to a number of grayscale and color images. The obtained results from peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) computations show the performance of the proposed technique. Experimental evaluation also shows that the proposed scheme is able to withstand a number of attacks such as JPEG compression, mean and median attacks. In our evaluation of the proposed technique, we used another technique of secure image watermarking based on DWT-2D and SVD.

scholarly journals A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1599
Author(s):  
Bowen Wu ◽  
Liangkuan Zhu ◽  
Jun Cao ◽  
Jingyu Wang
Keyword(s):  
Image Segmentation ◽  
Time Delay ◽  
Optimization Algorithm ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Evolutionary State

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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