Bilateral filter

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

Research of 3D reconstruction and filtering algorithm based on depth information of Kinect

Abstract: This paper analyzed and improved 3D reconstruction algorithm using the depth information from Kinect.To reduce noise,it proposed an improved bilateral filtering algorithm based on the signal structure.This new algorithm used a two-valued function to compute the weights of the filter,because the range of depth image data was already known.It also combined the RGB values and depth information of surrounding pixels to complement some missing depth information.The results show that the proposed algorithm has much better performance and efficiency,namely 6 times as fast as the original algorithm.

Video enhancement : content classification and model selection

Abstract: The purpose of video enhancement is to improve the subjective picture quality. The field of video enhancement includes a broad category of research topics, such as removing noise in the video, highlighting some specified features and improving the appearance or visibility of the video content. The common difficulty in this field is how to make images or videos more beautiful, or subjectively better. Traditional approaches involve lots of iterations between subjective assessment experiments and redesigns of algorithm improvements, which are very time consuming. Researchers have attempted to design a video quality metric to replace the subjective assessment, but so far it is not successful. As a way to avoid heuristics in the enhancement algorithm design, least mean square methods have received considerable attention. They can optimize filter coefficients automatically by minimizing the difference between processed videos and desired versions through a training. However, these methods are only optimal on average but not locally. To solve the problem, one can apply the least mean square optimization for individual categories that are classified by local image content. The most interesting example is Kondo’s concept of local content adaptivity for image interpolation, which we found could be generalized into an ideal framework for content adaptive video processing. We identify two parts in the concept, content classification and adaptive processing. By exploring new classifiers for the content classification and new models for the adaptive processing, we have generalized a framework for more enhancement applications. For the part of content classification, new classifiers have been proposed to classify different image degradations such as coding artifacts and focal blur. For the coding artifact, a novel classifier has been proposed based on the combination of local structure and contrast, which does not require coding block grid detection. For the focal blur, we have proposed a novel local blur estimation method based on edges, which does not require edge orientation detection and shows more robust blur estimation. With these classifiers, the proposed framework has been extended to coding artifact robust enhancement and blur dependant enhancement. With the content adaptivity to more image features, the number of content classes can increase significantly. We show that it is possible to reduce the number of classes without sacrificing much performance. For the part of model selection, we have introduced several nonlinear filters to the proposed framework. We have also proposed a new type of nonlinear filter, trained bilateral filter, which combines both advantages of the original bilateral filter and the least mean square optimization. With these nonlinear filters, the proposed framework show better performance than with linear filters. Furthermore, we have shown a proof-of-concept for a trained approach to obtain contrast enhancement by a supervised learning. The transfer curves are optimized based on the classification of global or local image content. It showed that it is possible to obtain the desired effect by learning from other computationally expensive enhancement algorithms or expert-tuned examples through the trained approach. Looking back, the thesis reveals a single versatile framework for video enhancement applications. It widens the application scope by including new content classifiers and new processing models and offers scalabilities with solutions to reduce the number of classes, which can greatly accelerate the algorithm design.

A non-local bilateral filter for image denoising

Abstract: In this paper, under the non-local means framework, we propose a non-local bilateral filter algorithm for image denoising based on the neighborhoods' gray value and the corresponding neighborhoods' Gaussian curvature. We also adopt a new method to provide the optimum denoising parameter h based on the discrete wavelet transform and the smoothing spline estimation. Meanwhile the generalized cross-validation criterion is adopted to give the parameter in smoothing spline estimation. Experiment results demonstrate that the proposed method is robust and efficient for both additive and multiplicative noise.

Computer-aided detection of lung nodules

Abstract: The invention relates to a method and a device for forming an image of body structures from an image data set, notably for highlighting potential nodular structures (KI; KA) in a lung The problem to be solved by the invention is to achieve automatic highlighting of potential nodular structures in methods of this kind This is realized in that in a plurality of steps a binary data set is formed in which all pixels present in the image data set are subdivided into pixels to be marked and those not to be marked, a first filtering operation being performed in which for each pixel (D) there is determined a distance value which corresponds to the shortest distance between the pixel and the edge (KAG) of the image structure (KA) in which the pixel is situated, those pixels being selected from the binary data set whose distance value is below a predetermined distance limit value, there being performed a second filtering operation in which those previously selected pixels remain selected which are directly neighbored by two pixels having a smaller distance value in both directions of at least one straight line which extends through the pixel, there being performed a third filtering operation in which those previously selected pixels remain selected for which the surrounding pixels, being situated at a distance corresponding to the distance value of the pixel, have a distance value which is a predetermined distance difference value smaller than the distance value of the pixel to be tested itself, the pixels thus selected being used to form an image in which the selected pixels are highlighted