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Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper. [ABSTRACT FROM AUTHOR]

Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, Free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB. [ABSTRACT FROM AUTHOR]

In this study, a new watermarking system was proposed to address two major concerns: slow learning and computational capability. At first, cover image was transformed into wavelet environment utilizing Integer Wavelet Transform (IWT) that makes the cover image free from false errors. Then, Grey Wolf Optimizer (GWO) was utilized for selecting the image pixels to embed the secret image in the cover image. GWO effectively selects the pixels utilizing fitness function, which calculates entropy, pixel intensity and edge of the cover images. Besides, the secret image was encrypted by using chaos encryption with hybrid mapping (logistic and henon map) that improves computation efficiency and security with good embedding capacity. After the cover image transformation and secret image encryption, Least Significant Bit (LSB) was utilized to deliver self-recovery features and also for locating the tempered region in the digital image. Experimental results showed that the developed system attained a secure transmission network with low complexity in light of Unified Averaged Changed Intensity (UACI), entropy value, Structural Similarity Index (SSIM), Normalized Cross Correlation (NCC), and Peak Signal-to-Noise Ratio (PSNR). Compared to the existing systems, the proposed system showed 3dB to 4.4 dB improvement in PSNR and 0.32 value improvement in SSIM. [ABSTRACT FROM AUTHOR]

Nowadays, all the medical diagnosis is achieved by using the Digital Image Processing (DIP). Because, the usage of DIP is more important in the medical field to identify the activities of the patients related to various diseases. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) scan images are used to perform the fusion process. In brain medical image, MRI scan is used to show the brain structural information without functional data. But, CT scan image is included the functional data with brain activity. To improve the low dose CT scan, Hybrid algorithm is introduced in this paper which is implemented in FPGA. The main objective of this work is to optimize performances of the hardware. This work is implemented in FPGA. The combination of Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) is known as hybrid algorithm. The Maximum Selection Rule (MSR) is used to select the high frequency component from DWT. These three algorithms have RTL architecture which is implemented by Verilog code. Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) performances analysed for the different methods. In 180nm technology, DWT-PCA- IF architecture achieved 5.145mm2 area, 298.25mW power, and 124ms delay. From the fused medical image, Mean, Standard Deviation (SD), Entropy, and Mutual information (MI) performances are evaluated for DWT-PCA method which has better performance than conventional methods. [ABSTRACT FROM AUTHOR]