Wavelet-based denoising for 3D OCT images

Optical coherence tomography produces high resolution medical images based on spatial and temporal coherence of the optical waves backscattered from the scanned tissue. However, the same coherence introduces speckle noise as well; this degrades the quality of acquired images. In this paper we propose a technique for noise reduction of 3D OCT images, where the 3D volume is considered as a sequence of 2D images, i.e., 2D slices in depth-lateral projection plane. In the proposed method we first perform recursive temporal filtering through the estimated motion trajectory between the 2D slices using noise-robust motion estimation/compensation scheme previously proposed for video denoising. The temporal filtering scheme reduces the noise level and adapts the motion compensation on it. Subsequently, we apply a spatial filter for speckle reduction in order to remove the remainder of noise in the 2D slices. In this scheme the spatial (2D) speckle-nature of noise in OCT is modeled and used for spatially adaptive denoising. Both the temporal and the spatial filter are wavelet-based techniques, where for the temporal filter two resolution scales are used and for the spatial one four resolution scales. The evaluation of the proposed denoising approach is done on demodulated 3D OCT images on different sources and of different resolution. For optimizing the parameters for best denoising performance fantom OCT images were used. The denoising performance of the proposed method was measured in terms of SNR, edge sharpness preservation and contrast-to-noise ratio. A comparison was made to the state-of-the-art methods for noise reduction in 2D OCT images, where the proposed approach showed to be advantageous in terms of both objective and subjective quality measures

A novel filter for block-based motion estimation

Noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors using block based motion estimation techniques. Similar noises are further introduced when the technique of global motion compensation is applied to obtain 'true' object motion from video sequences, where both the camera and object motions are present. We observe that the performance of the mean and the median filters in removing false motion vectors, for estimating 'true' object motion, is not satisfactory, especially when the size of the object is significantly smaller than the scene. In this paper we introduce a novel filter, named as the Mean-Accumulated-Thresholded (MAT) filter, in order to capture 'true' object motion vectors from video sequences with or without the camera motion (zoom and/or pan). Experimental results on representative standard video sequences are included to establish the superiority of our filter compared with the traditional median and mean filters

Automatic Feature-Based Stabilization of Video with Intentional Motion through a Particle Filter

Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorith

Real-Time Restoration of Images Degraded by Uniform Motion Blur in Foveal Active Vision Systems

Foveated, log-polar, or space-variant image architectures provide a high resolution and wide field workspace, while providing a small pixel computation load. These characteristics are ideal for mobile robotic and active vision applications. Recently we have described a generalization of the Fourier Transform (the fast exponential chirp transform) which allows frame-rate computation of full-field 2D frequency transforms on a log-polar image format. In the present work, we use Wiener filtering, performed using the Exponential Chirp Transform, on log-polar (fovcated) image formats to de-blur images which have been degraded by uniform camera motion.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-96-C-0178); Office of Naval Research Multidisciplinary University Research Initiative (N00014-95-1-0409