Bilateral filter

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

A Generalized DAMRF Image Modeling for Superresolution of License Plates

Abstract: In this paper, we propose a novel superresolution (SR) reconstruction algorithm to handle license plate texts in real traffic videos. To make license plate numbers more legible, a generalized discontinuity-adaptive Markov random field (DAMRF) model is proposed based on the recently reported bilateral filtering, which not only preserves edges but is robust to noise as well. Moreover, instead of looking for a fixed value for the regularization parameter, a method for automatically estimating it is applied to the proposed model based on the input images. Information needed to determine the regularization parameter is updated at each iteration step, which is based on the available reconstructed image. Finally, we use the graduated nonconvexity optimization procedure to minimize the cost function. Results on synthetic and real traffic sequences are presented, which show the effectiveness of the proposed method and demonstrate its superiority to the conventional DAMRF SR method.

Blending multiple images using local and global information

Abstract: A method blends multiple input images into an output image for any arbitrary view. In the output images, pixels that are produced from only a single input pixel are identified. The weight of the single pixels is set to one. For each remaining pixel in the input images with unassigned weights distances to an image and a depth boundary are measured, and proportional weight, in a range from zero to one, for these remaining pixels are set proportional to the minimum of the two distances. Then, each input image is rendered to the output image according to the blending fields.

Small Target Detection Using Bilateral Filter Based on Edge Component

Abstract: Bilateral filter (BF) performs sharpness enhancement and noise removal by using two Gaussian filters, the domain filter in spatial domain and the range filter in intensity domain. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF for small target detection with the adaptive standard deviation based on the analysis of the edge component, also having the variable filter size. At first, threshold of pixel edge components for four directions decides whether any pixel is potential small targets or not. For the proposed BF operation for the potential small target pixels, its edge component is mapped to two standard deviations of the domain filter and the range filter in the proposed BF by mapping function. When the BF comes to a target region, the two standard deviations increase in proportion to the edge component to blur the small targets. To further blur the small targets, the filter size of the BF also increases by its edge component. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.

A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

Abstract: In this paper, we present a new approach of multi-frame super-resolution (SR). The SR techniques strongly depend on the availability of accurate motion estimation. When the estimation of motion is not well established, as usually happens for non-parametric motion, annoying artifacts appear in the super-resolved image. Since SR problems suffer from the motion and blur estimations, new techniques are considered to improve the registration and restoration steps. The proposed method consists of a non-parametric image registration based on diffusion regularization and a nonlocal Laplace regularizer combined with a bilateral filter (BTV) in the reconstruction step to remove noise and motion outliers. The diffusion registration is employed to handle the small deformation between the unregistered images, while the combination of nonlocal Laplace and BTV is used to increase the robustness of the restoration step with respect to the blurring effect and to the noise. We also prove the existence of a solution to the well posed registration problem. Simulation results using different images show the effectiveness and robustness of our algorithm against noise and outliers compared to other existing methods.

Digital image appearance enhancement and compressibility improvement method and system

Abstract: A method and system for processing a digital image and improving the appearance of the image while enhancing the compressibility of the image. The digital image has a plurality of input pixels. The image processing system has a filter selection mechanism for receiving a filter selection window corresponding to a current input pixel and responsive thereto for generating a filter identifier based on either an edge parameter computed based on the filter selection window or an activity metric computed based on the filter selection window. A filter application unit that is coupled to the filter selection mechanism for receiving the filter identifier and applying a filter identified by the filter identifier to an input pixel window to generate an output pixel is also provided.