9,375 research outputs found
Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain
In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. ABSTRACT In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
Adaptive Smoothing of Digital Images: The R Package adimpro
Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl and Spokoiny 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System
The paper focuses on the problem of vision-based obstacle detection and
tracking for unmanned aerial vehicle navigation. A real-time object
localization and tracking strategy from monocular image sequences is developed
by effectively integrating the object detection and tracking into a dynamic
Kalman model. At the detection stage, the object of interest is automatically
detected and localized from a saliency map computed via the image background
connectivity cue at each frame; at the tracking stage, a Kalman filter is
employed to provide a coarse prediction of the object state, which is further
refined via a local detector incorporating the saliency map and the temporal
information between two consecutive frames. Compared to existing methods, the
proposed approach does not require any manual initialization for tracking, runs
much faster than the state-of-the-art trackers of its kind, and achieves
competitive tracking performance on a large number of image sequences.
Extensive experiments demonstrate the effectiveness and superior performance of
the proposed approach.Comment: 8 pages, 7 figure
A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves
This paper presents a method for extraction and analysis of curve--type
structures which consist of disconnected components. Such structures are found
in electron--microscopy (EM) images of metal nanograins, which are widely used
in the field of nanosensor technology.
The topography of metal nanograins in compound nanomaterials is crucial to
nanosensor characteristics. The method of completing such templates consists of
three steps. In the first step, a local Gaussian filter is used with different
weights for each neighborhood. In the second step, an adaptive morphology
operation is applied to detect the endpoints of curve segments and connect
them. In the last step, pruning is employed to extract a curve which optimally
fits the template
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