This paper discusses removing large objects from digital images and fills the hole that is left behind in a visually plausible way. We present a novel and efficient algorithm that fills the hole by exemplar-based synthesis. Here the simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm. The texture image is repaired by the exemplar -based method; for the structure image, the Laplacian operator is employed to enhance the structure information, and the Laplacian image is inpainted by the exemplar-based algorithm, followed by a reconstruction based on the Poisson equation. To improve the computational efficiency of our algorithm we go for successive elimination algorithm (SEA). In 8 pixel neighborhood method, identifying central pixel value by investigating surrounded 8 neighborhood pixel properties like color variation, repetition, intensity and direction. Finally we compare speed and accuracy of a picture enhancement using 8 pixel neighborhood with exemplar based poisson & successive elimination method

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Diabetic Retinopathy (DR) is quite possibly the main widely recognized diabetic disease found in the vast majority. Advancement of diabetic retinopathy is grouped by its seriousness. Be that as it may, critical lacks of master spectators have incited supercomputer helped observing frameworks to distinguish the DR. In retinopathy, the kind of vascular organization of the natural eye is a crucial indicator element. This study provides a method for recognizing exudates and veins in retinal images for the purpose of examining the retinal vasculature. Convolution Neural Network (CNN) is used for image identification and preparation of retinal images following image processing stages to arrange the retinal fundus images. The proposed recognizing diabetics by fundus retinal picture arrangement utilizing return for capital invested (Region of Interest) assumes significant parts in recognition of certain illnesses in beginning phase diabetes by contrasting its exactness and existing strategies like the conditions of retinal veins.

Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer. This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to be an achievement in the field of health care monitoring systems.

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.