Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

Adaptive image filtering based on a distance transform

Abstract: A method for reducing visual artifacts in reconstructed images. One embodiment of the method determines edge energy for each pixel in the image and then compares the edge energy for each pixel to a threshold, producing an edge map. A distance transform is then used to produce a filter map and a filter is applied to pixel in the image, such that the filter applied is dependent upon a filter map value for each pixel. An output value for each pixel is then produced.

Single image fog removal using gamma transformation and median filtering

Abstract: In this paper, a novel and simple restoration-based fog removal approach is proposed. Here, we proposed an approach which is based on gamma transformation method and median filter. Transmission map is refined by the gamma transformation method. Then obtained transmission map is smoothed by a median filter. Qualitative and quantitative analysis demonstrate that proposed algorithm performs well in comparison with bilateral filtering. It can handle color as well as gray images. Proposed algorithm, due to its speed and ability to improve visibility, may be used in many systems, ranging from tracking and navigation, surveillance, consumer electronics, intelligent vehicles to remote sensing.

Augmented Likelihood Image Reconstruction

Abstract: The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.