Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
This paper proposes a technique for medical picture fusion based on the guided image filter. It utilizes guided filtering to smooth images, using texture information as guidance for the filter. Weight maps of the detail images are created through pixel weight computation based on image statistics. Finally, the source images are combined using a weighted average combining approach. The effectiveness of the proposed method is evaluated in terms of multiple quantitative image quality assessment factors and compared with several state-of-the-art image fusion algorithms. Experimental results indicate that the suggested approach for image fusion is effective.
In the age of artificial intelligence, remote sensing, and especially satellite imagery, are gaining widespread interest among the computer science community in their effort to give machines the ability to recognize their environment through satellite image classification. Imaging satellites provide images of Earth that are collected, analyzed, and processed for civil and military purposes. Satellite images are an important source of data captured from artificial satellites revolving around the Earth's orbit. These images are susceptible to noise and irregular illumination, which affect image quality. An improved enhancement technique is proposed in this paper to increase the visual perception of the image while preserving details. In this method, we use image processing techniques for contrast enhancement. Contrast enhancement techniques greatly support the creation of high-quality contrast images. The effectiveness of the proposed method is tested using PSNR, Entropy, and histogram analysis.
