Median filter

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

Estimation of images degraded by film-grain noise

Abstract: Film-grain noise describes the intrinsic noise produced by a photographic emulsion during the process of image recording and reproduction. In this paper we consider the restoration of images degraded by film-grain noise. First a detailed model for the over-all photographic imaging system is presented. The model includes linear blurring effects and the signal-dependent effect of film-grain noise. The accuracy of this model is tested by simulating images according to it and comparing the results to images of similar targets that were actually recorded on film. The restoration of images degraded by film-grain noise is then considered in the context of estimation theory. A discrete Wiener filer is developed which explicitly allows for the signal dependence of the noise. The filter adaptively alters its characteristics based on the nonstationary first order statistics of an image and is shown to have advantages over the conventional Wiener filter. Experimental results for modeling and the adaptive estimation filter are presented.

Nonlinear Optical Image Filtering By Time-Sequential Threshold Decomposition

Abstract: Shape-changing, or morphological, transformations (e.g., erosion, dilation, median filtering, and other ranked-order filtering) on binary imagery can be obtained by optically convolving the input image with a disk or other binary spread function and thresholding the output. Gray-scale images can be processed if the input is decomposed into a sequence of binary "slices" by a variable thresholding operation (threshold decomposition). The slices undergo shape-changing transformations and are then added together to produce the output gray-scale image. Median filtering to remove "salt-and-pepper" noise from a gray-scale image is demonstrated. The use of gray-scale (as opposed to binary) convolution kernels allows extension of the method to a more general class of nonlinear filtering operations that includes stack filters.

Effective Strip Noise Removal for Low-Textured Infrared Images Based on 1-D Guided Filtering

Abstract: Infrared images typically contain obvious strip noise. It is a challenging task to eliminate such noise without blurring fine image details in low-textured infrared images. In this paper, we introduce an effective single-image-based algorithm to accurately remove strip-type noise present in infrared images without causing blurring effects. First, a 1-D row guided filter is applied to perform edge-preserving image smoothing in the horizontal direction. The extracted high-frequency image part contains both strip noise and a significant amount of image details. Through a thermal calibration experiment, we discover that a local linear relationship exists between infrared data and strip noise of pixels within a column. Based on the derived strip noise behavioral model, strip noise components are accurately decomposed from the extracted high-frequency signals by applying a 1-D column guided filter. Finally, the estimated noise terms are subtracted from the raw infrared images to remove strips without blurring image details. The performance of the proposed technique is thoroughly investigated and is compared with the state-of-the-art 1-D and 2-D denoising algorithms using captured infrared images.