Median filter

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

A simple method of computing the input quantization and multiplication roundoff errors in a digital filter

Abstract: A simple method of calculating the steady-state value of the variance of the output noise of a digital filter due to the input quantization noise or internally generated noise from product round-off is presented. The output noise is expressed as a sum of simpler terms belonging to one of four basic groups. Explicit expressions have been developed for rapid evaluation of these terms in the expansion. The method is illustrated by means of examples.

Nested image steganography scheme using QR-barcode technique

Abstract: In this paper, QR bar code and image processing techniques are used to construct a nested steganography scheme. There are two types of secret data lossless and lossy embedded into a cover image. The lossless data is text that is first encoded by the QR barcode; its data does not have any distortion when comparing with the extracted data and original data. The lossy data is a kind of image; the face image is suitable for our case. Because the extracted text is lossless, the error correction rate of QR encoding must be carefully designed. We found a 25% error correction rate is suitable for our goal. In image embedding, because it can sustain minor perceptible distortion, we thus adopted the lower nibble byte discard of the face image to reduce the secret data. When the image is extracted, we use a median filter to filter out the noise and obtain a smoother image quality. After simulation, it is evident that our scheme is robust to JPEG attacks. Compared to other steganogra- phy schemes, our proposed method has three advantages: i the nested scheme is an enhanced security system never previously developed; ii our scheme can conceal lossless and lossy secret data into a cover image simultaneously; and iii the QR barcode used as secret data can widely extend this method's application fields. © 2009 Society of Photo-Optical

Image noise reduction

Abstract: An image processing apparatus determines whether or not a target pixel TP which is the object of the smoothing process is an edge-forming pixel based on the edge level (gradient g). If the pixel is determined to be an edge-forming pixel, the smoothing process employs an elliptical median filter instead of a moving average filter. The median filter MF has an elliptical reference area RA that is tilted (angled) to match the orientation of the edge Eg (edge angle), allowing the smoothing process to be carried out without compromising the edge components. The angle of the edge Eg is thus determined in the smoothing process, a median filter MF with an angle matching the determined edge angle is selected, and the selected median filter MF is used to carry out the smoothing process on target pixels TP which are edge-forming pixels.

Assessment of noise in a digital image using the join-count statistic and the Moran test

Abstract: It is assumed that the data bits of a pixel in digital images can be divided into signal and noise bits. The signal bits occupy the most significant part of the pixel and the noise bits the least significant part. The signal part of each pixel are correlated while the noise parts are uncorrelated. Two statistical methods, the Moran test and the join-count statistic, are used to examine the noise parts. Images from three digital modalities-computerized tomography, magnetic resonance and computed radiography-are used for the evaluation of the noise bits. A residual image is formed by subtracting the original image from its smoothed version. The noise level in the residual image is then identical to that in the original image. Both statistical tests are then performed on the bit planes of the residual image. The results show that most digital images contain only 8-9 bits of correlated information.