Denoising Vanilla Autoencoder for RGB and GS Images with Gaussian Noise.

Noise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represent the image captured are altered, which is translated into a loss of information. In this way, there are required procedures to recover data information closest to the real scene.

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators.

In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities.

Analysis of Data Reception in the Communication Layer Applied to an Architecture of Mobile Sensor Networks in Marine Environments.

This paper is focused on the use of radio frequency identification (RFID) technology operating at 125 kHz in a communication layer for a network of mobile and static nodes in marine environments, with a specific focus on the Underwater Internet of Things (UIoT). The analysis is divided into two main sections: characterizing the penetration depth at different frequencies and evaluating the probabilities of data reception between antennas of static nodes and a terrestrial antenna considering the line of sight (LoS) between antennas. The results indicate that the use of RFID technology at 125 kHz allows for data reception with a penetration depth of 0.

Efficient System for Delimitation of Benign and Malignant Breast Masses.

In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses' regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors.

Rician Denoising Based on Correlated Local Features LMMSE Approach.

In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. A closed-form analytical solution for our estimator is presented and it proves to make the filtering process far simpler and faster than other estimation techniques that rely on iterative optimization scheme and require multiple data samples. An experimental validation of our correction scheme was carried out through large scale experiments using both clinical and synthetic MR images, artificially corrupted with rician noise of σ varying from 1 to 40.

PhotoId-Whale: Blue whale dorsal fin classification for mobile devices.

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering.

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation.