HANIS
@ssn.edu.in
Scopus Publications
Scopus Publications
S. Hanis, S. Abinav Narayanan, P. Abishek Viswanath, and V. Bhooshan
World Scientific Pub Co Pte Ltd
In this paper, we present a deep reinforcement learning-based method for effectively denoising satellite and aerial imagery data. Noise of various kinds and with varying noise levels contaminates satellite imagery data. The image’s quality and readability suffer when there is noise present. Therefore, it is crucial to create a network that can effectively and efficiently remove noise from the image while also preserving its quality and signal components. This paper evaluates the denoising capabilities of the deep reinforcement learning system. The proposed network is trained using the training set from the “dataset of object detection in aerial images (DOTA) dataset,” and its hyperparameters were adjusted for optimum performance. The training set from the aforementioned dataset was used to train the proposed network. The trained network was given the test set of unseen images for denoising. Statistical denoising, a common denoising technique, was used on the test dataset, and the outcomes were assessed. The same unseen images were also given to existing CNN-based denoising algorithms like denoising using CNN (DnCNN), U-shaped DnCNN (UDnCNN), and dilated U-shaped DnCNN (DUDnCNN), designed specifically for image denoising. Runtime and structural similarity index (SSIM) as well as peak signal-to-noise ratio have both been used as evaluation metrics to compare the effectiveness of various approaches. It is discovered that, when comparing the performance of various systems, the suggested system outperforms both statistical- and CNN-based denoising in terms of the evaluation metrics, PSNR and SSIM.
S. Hanis
Computers, Materials and Continua (Tech Science Press)
Hanis Stanley and Amutha Ramachandran
Walter de Gruyter GmbH
Abstract A novel extended logistic map has been proposed and tested mathematically for security-based applications. Because the designed extended logistic map behaves chaotically across a wide range of logistic control parameters, it is extremely difficult to predict using even the most exhaustive search methods. The map overcomes a significant drawback of simple logistic mapping, which is commonly used in encryption algorithms. The chaotic map designed was also used as a key to shuffle the pixel position of the image for the image shuffling algorithm developed. The algorithm developed produced excellent results and is adequate for providing an encrypted image in resource-constrained systems. Performance results show that this map is highly chaotic and provides high security when applied in image encryption systems.
Nolen B. Bryant, Kwok K. Chung, Jie Feng, Sommer Harris, Kristine N. Umeh, and Michal Aibin
IEEE
The rapid increase in bandwidth demand has driven the development of flexible, efficient, and scalable optical networks. One of the technologies that allows for much more flexible resource utilization is Elastic Optical Network. However, there is a need to solve the Routing, Modulation and Spectrum Assignment (RMSA) problem. In this paper, we use reinforcement learning to improve the efficiency of the routing algorithm. More specifically, we implement an off-policy Q-learning and compare it with the state-of-the-art algorithms. The results confirm that Q-learning is highly effective when optimal results need to be found in a large search space.
S. Hanis, N. Edna Elizabeth, R. Kishore, and Ala Khalifeh
Springer International Publishing
Sukanthi, S. Sakthivel Murugan, and S. Hanis
World Scientific Pub Co Pte Ltd
India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered.
Stanley Hanis and Ramachandran Amutha
Springer Science and Business Media LLC
S. Hanis and R. Amutha
Springer Science and Business Media LLC
L. Donisha Greet and S. Harris
IEEE
A sequence of radar images containing the target is collected using an avian radar system. Radar image is composed of complex background and the target (flying birds, aircrafts, missiles etc). The main objective is to detect the target using Fast Independent Component Analysis (Fast ICA). Fast ICA separates the background and target from the radar image. Constant False Alarm Rate (CFAR) determines the power threshold above which the return echoes have been originated from the target. Clutter suppression is done by Conventional CFAR segmentation method and Two parameter CFAR methods. A new scheme based on Fusion of Conventional CFAR and Two Parameter CFAR is adopted and an average detection of 85% is obtained. After fusion, the target is detected from the radar images.