Adaptive pre-filtering techniques for colour image analysis

One important step in the process of colour image segmentation is to reduce the errors caused by image noise and local colour inhomogeneities. This can be achieved by filtering the data with a smoothing operator that eliminates the noise and the weak textures. In this regard, the aim of this paper is to evaluate the performance of two image smoothing techniques designed for colour images, namely bilateral filtering for edge preserving smoothing and coupled forward and backward anisotropic diffusion scheme (FAB). Both techniques are non-linear and have the purpose of eliminating the image noise, reduce weak textures and artefacts and improve the coherence of colour information. A quantitative comparison between them will be evaluated and also the ability of such techniques to preserve the edge information will be investigated

Automatic detection of arcs and arclets formed by gravitational lensing

We present an algorithm developed particularly to detect gravitationally lensed arcs in clusters of galaxies. This algorithm is suited for automated surveys as well as individual arc detections. New methods are used for image smoothing and source detection. The smoothing is performed by so-called anisotropic diffusion, which maintains the shape of the arcs and does not disperse them. The algorithm is much more efficient in detecting arcs than other source finding algorithms and the detection by eye.Comment: A&A in press, 12 pages, 16 figure

BLADE: Filter Learning for General Purpose Computational Photography

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

MRI diffusion-based filtering: a note on performance characterisation

Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality

MRI diffusion-based filtering: a note on performance characterisation

Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality