Median filter

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

On Detecting Jumps in Time Series - Nonparametric Setting

Abstract: Motivated by applications in statistical quality control and signal analysis, we propose a sequential detection procedure which is designed to detect structural changes, in particular jumps, immediately. This is achieved by modifying a median filter by appropriate kernel-based jump-preserving weights (shrinking) and a clipping mechanism. We aim at both robustness and immediate detection of jumps. Whereas the median approach ensures robust smooths when there are no jumps, the modification ensure immediate reaction to jumps. For general clipping location estimators, we show that the procedure can detect jumps of certain heights with no delay, even when applied to Banach space-valued data. For shrinking medians, we provide an asymptotic upper bound for the normed delay. The finite sample properties are studied by simulations which show that our proposal outperforms classical procedures in certain respects.

An improved recursive and adaptive median filter for high density impulse noise

Abstract: An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.

Imaging device, image processing device, image processing method, program for image processing method, and recording medium having program for image processing method recorded thereon

Abstract: A method and apparatus to remove color noise included in raw data while effectively preventing image quality degradation. For Interest pixels serially set onto a mosaic image formed of raw data, conversion is executed into a pixel value for noise removal based on a processing reference pixel value having a unified color signal component in each interest pixel, noise is removed from the pixel value for noise removal, and the pixel value for noise removal with noise removed is converted into the source pixel value, whereby only color noise can be removed without affecting a luminance signal.

Error Detection Technique for a Median Filter

Abstract: In digital image processing systems, the acquisition stage may capture impulsive noise along with the image. This physical phenomenon is commonly referred to as “salt-and-pepper” noise. The median filter is a nonlinear image processing operation used to remove this impulsive noise from images. This digital filter can be implemented in hardware to speed up the algorithm. However, an SRAM-based field-programmable gate array implementation of this filter is then susceptible to configuration memory bit flips induced by single event upsets, so a protection technique is needed for critical applications in which the proper filter operation must be ensured. In this paper, a fault-tolerant implementation of the median filter is presented and studied in-depth. Our protection technique checks if the median output is within a dynamic range created with the remaining nonmedian outputs. An output error signal is activated if a corrupted image pixel is detected, then a partial or complete reconfiguration can be performed to remove the configuration memory error. Experimental results show that our technique detects enough corrupted pixels in an image to prevent 91% of the corrupted images from being erroneously sent to the next image processing operation. This high error detection rate is achieved introducing only a 35% of additional resource overhead.