Median filter

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

Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology

Abstract: This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.

adaptive fourier threshold filtering: A method to reduce noise and incoherent artifacts in high resolution cardiac images

Abstract: We introduce an image processing method which reduces white noise and random artifacts in sets of high resolution, time resolved images. At each pixel, the processing consists of: 1) the isolation of a time intensity curve (TIC), 2) Fourier transformation of each TIC, 3) application of a threshold to remove low intensity coefficients, 4) inverse transformation to generate noise reduced TICs which are recombined to form images with improved signal-to-noise ratio (SNR). Noise filtering by Fourier thresholding is demonstrated on a set of cardiac images, resulting in a reduction of the noise energy by approximately 90%.

An efficient non-linear cascade filtering algorithm for removal of high density salt and pepper noise in image and video sequence

Abstract: In this paper, an efficient non-linear cascade filter for the removal of high density salt and pepper noise in image and video is proposed. The proposed method consists of two stages to enhance the filtering. The first stage is the Decision based Median Filter (DMF) which is used to identify pixels likely to be contaminated by salt and pepper noise and replaces them by the median value. The second stage is the Unsymmetric Trimmed Filter, either Mean Filter (UTMF) or Midpoint Filter (UTMP) which is used to trim the noisy pixels in an unsymmetric manner and processes with the remaining pixels The basic idea is that, though the level of denoising in the first stage is lesser at high noise densities, the second stage helps to increase the noise suppression. Hence, the proposed cascaded filter, as a whole is very suitable for low, medium as well as high noise densities even above 90%. The existing non-linear filters such as Standard Median Filter (SMF), Adaptive Median Filter (AMF), Weighted Median Filter (WMF), Recursive Weighted Median Filter (RWM) performs well only for low and medium noise densities. The recently proposed Decision Based Algorithm (DBA) shows better results upto 70% noise density and at high noise densities, the restored image quality is poor. The proposed algorithm shows better image and video quality in terms of visual appearance and quantitative measures.

Study on conditioning and feature extraction algorithm of photoplethysmography signal for physiological parameters detection

Abstract: Photoplethysmography(PPG) signal can reflect many physiological parameters, such as heart function, blood vessel elasticity, blood viscosity and so on. It was a novel noninvasive method with the advantage of convenience and accuracy. It was important to find efficient pre-processing and feature extraction algorithms to deal with original PPG signal, which was interfered by many other factors. Many practical methods including median filtering and FIR filtering was used. A new algorithm based on wavelet transformation was proposed for eliminating the baseline drift. Feature points extraction was another key issue. An improved differential algorithm was used to solve this problem. All of these practical algorithms provided an effective platform for physiological parameters detection.

Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation

Abstract: Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.