The images obtained by X-Ray or computed tomography (CT) in adverse conditions may be contaminated with noise that can affect the detection of diseases. A large number of image processing techniques (filters) have been proposed to remove noise. These techniques depend on the type of noise present in the image. In this work, we propose a method designed to reduce the Gaussian, the impulsive and speckle noise and combined noise. This filter, called PGNDF, combines a non-linear diffusive filter with a peer group with fuzzy metric technique. The proposed filter is able to reduce efficiently the image noise without any information about what kind of noise might be present. To evaluate the filter performance, we use mammographic images from the mini-MIAS database which we have damaged by adding Gaussian, impulsive and speckle noises of different magnitudes. As a result, the proposed method obtains a good performance in most of the different types of noise.

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a comparison of the implementation parallelised in multi-core, GPUs and the combination of both is conducted. A performance analysis with large images is conducted in order to identify the amount of pixels to allocate in the CPU and GPU. The observed time shows that both devices must have work to do, leaving the most to the GPU. Results show that parallel implementations of denoising filters on GPUs and multi-cores are very advisable, and they open the door to use such algorithms for real-time processing.This work was supported by the Spanish Ministry of Science and Innovation [Project TIN2011-26254]. M. G. Sánchez would like to acknowledge DGEST ITCG for the scholarship awarded through the PROMEP program

In this study, a low-cost proposed platform for training dynamics (PPTD) is proposed based on operational amplifiers to understand the dynamics and variables of the agricultural tractor John Deere tractor model 4430 to gain autonomy and analyze the behavior of control algorithms proposed in real time by state feedback. The proposed platform uses commercial sensors and interacts with the Arduino Uno and/or Daq-6009 board from National Instruments. A mobile application (APP) was also developed for real-time monitoring of autonomous control signals, the local reference system, and physical and dynamic variables in the tractor; this platform can be used as a mobile alternative applied to a tractor in physically installed form. In the presented case, the PPTD was mounted on a John Deere tractor to test its behavior; moreover, it may be used on other tractor models similarly as established here. The established results of this platform were compared with models established in MATLAB, validating the proposal. All simulations and developments are shared through a web-link as open-source files so that anyone with basic knowledge of electronics and modeling of vehicles can reproduce the proposed platform.

In this paper, we address the problem of impulsive noise reduction in color images through an evolutionary approach. We designed a new hybrid genetic algorithm, called GARIN, which takes as input a noisy image and generates as output a reduced noise version of the same image. As part of its evolutionary process, GARIN integrates the execution of robust and adaptive filters from literature with the aim to combine and improve their results. Unlike most genetic algorithms designed for color image denoising, our algorithm evolves a set of images instead of the set of parameters of the filters. Experimental results show that, compared with other filters of literature, the algorithm GARIN efficiently removes impulsive noise in color images while, at the same time, it preserves their main features.