Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Vector directional filters-a new class of multichannel image processing filters

Abstract: Vector directional filters (VDF) for multichannel image processing are introduced and studied. These filters separate the processing of vector-valued signals into directional processing and magnitude processing. This provides a link between single-channel image processing where only magnitude processing is essentially performed, and multichannel image processing where both the direction and the magnitude of the image vectors play an important role in the resulting (processed) image. VDF find applications in satellite image data processing, color image processing, and multispectral biomedical image processing. Results are presented here for the case of color images, as an important example of multichannel image processing. It is shown that VDF can achieve very good filtering results for various noise source models. >

An improved Sobel edge detection

Abstract: This paper proposes a method which combines Sobel edge detection operator and soft-threshold wavelet de-noising to do edge detection on images which include White Gaussian noises. In recent years, a lot of edge detection methods are proposed. The commonly used methods which combine mean de-noising and Sobel operator or median filtering and Sobel operator can not remove salt and pepper noise very well. In this paper, we firstly use soft-threshold wavelet to remove noise, then use Sobel edge detection operator to do edge detection on the image. This method is mainly used on the images which includes White Gaussian noises. Through the pictures obtained by the experiment, we can see very clearly that, compared to the traditional edge detection methods, the method proposed in this paper has a more obvious effect on edge detection.

Median Filtering Forensics Based on Convolutional Neural Networks

Abstract: Median filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image median filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting median filtering from small-size and compressed image blocks, by taking into account of the properties of median filtering, we propose a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying CNNs in median filtering image forensics. Unlike conventional CNN models, the first layer of our CNN framework is a filter layer that accepts an image as the input and outputs its median filtering residual (MFR). Then, via alternating convolutional layers and pooling layers to learn hierarchical representations, we obtain multiple features for further classification. We test the proposed method on several experiments. The results show that the proposed method achieves significant performance improvements, especially in the cut-and-paste forgery detection.

Advanced Signal Processing and Digital Noise Reduction

Abstract: Introduction to Signal Processing and Noise Reduction Stochastic Processes and Statistical Characterization of Signals Signal Transforms Bayesian Probabilistic Estimation Theory Wiener Filters and Kalman Filters Linear Prediction Models Sample-Adaptive Least Squared Error Filters Power Spectrum Estimation Finite-State Statistical Models for Non-stationary Stochastic Processes Interpolation of a Sequence of Samples Modelling, Detection and Removal of Impulsive Noise Spectral Subtraction Removal of Transient Noise Pulses Echo Cancellation and Multi-Input Adaptive Noise Reduction Adaptive Notch Filters Channel Equalization Noise Compensation for Speech Recognition in Adverse Environments.

Vector filtering for color imaging

Abstract: Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.