A Simple Method for Removing Reflection and Distortion from a Single Image

Abstract: This paper deals with a problem of removing reflection and distortion from un-natural images. This will effected in the quality of images. Reflection happens when there is the variation in direction of a wave front at an interface between two different media so that the wave front returns into the medium from which it originated. The law of reflection describes for specular reflection the angle at which wave is reflected equals the angle at which it is incident on the surface. Mirrors exhibit specular reflection. In photograph Distortion will happens when either the properties of the lens or the position of the camera relative to the subject. Here the input contains multiple polarized images with different polarizer angles. The output consists of high quality distortion and reflection separation from images. In this paper proposed a Quality Assessment method Scheme (QAMS) for removing both reflection and distortion from images. Using this QAMS method, the quality of the image can be improved by measuring PSNR and Error Rate

A new secure transmission scheme between senders and receivers using HVCHC without any loss

Abstract This paper presents a novel secure medical image transmission scheme using hybrid visual cryptography and Hill cipher (HVCHC) between sender and receiver. The gray scale medical images have been considered as a secret image and split into different shares by visual cryptography (VC) encryption process. The split shares are once again encoded by Hill cipher (HC) encode process for improving the efficiency of the proposed method. In this process, the encrypted medical image (shares) pixels are converted as characters based on the character determination (CD) and lookup tables. In result, a secret image is converted into characters. These characters are sent to the receiver/authenticated person for the reconstruction process. In receiver side, the ciphertext has been decoded by HC decode process for reconstructing the shares. The reconstructed shares are decrypted by the VC decryption process for retaining the original secret medical image. The proposed algorithm has provided better CC, less execution time, higher confidentiality, integrity, and authentication (CIA). Therefore, using this proposed method, cent percent of the original secret medical image can be obtained and the secret image can be prevented from the interception of intruders/third parties

A Fuzzy Ga Based STATCOM for Power Quality Improvement

This paper deals with various power issues such as voltage sag, swell, harmonics, and surges using static synchronous compensator (STATCOM).The conventional controller suffers from uncertain parameters and non-linear qualities. However they are computationally inefficient extending to optimize the fuzzy controller (FC) parameters, since they exhaustively search the optimal values to optimize the objective functions. To overcome this drawback, a genetic algorithm (GA) based Fuzzy controller parameter optimization is presented in this paper. The GA algorithm is used to find the optimal fuzzy parameters for minimizing the objective functions. The feasibility of the proposed GA technique for distribution systems to improve the sag and total harmonic distortion (THD) as major power quality indices in sensitive loads at fault conditions has been simulated and tested. Therefore, the multi-objective optimization algorithm is considered in order to attain a better performance in solving the related problems

Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including pre-processing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrast-limited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inception-v3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%

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