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.

Numerical Analysis of Zirconium and Titanium Implants under the Effect of Critical Masticatory Load.

Dental implants have become an alternative to replace the teeth of people suffering from edentulous and meet the physiological and morphological characteristics (recovering 95% of the chewing function). The evolution and innovation of biomaterials for dental implants have had a trajectory that dates back to prehistory, where dental pieces were replaced by ivory or seashells, to the present day, where they are replaced by metallic materials such as titanium or ceramics such as zirconium or fiberglass. The numerical evaluation focuses on comparing the stress distribution and general displacement between different dental implants and a healthy tooth when applying a force of 850 N.

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.

Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising.

In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images.