EWA Splatting

In this paper, we present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.Engineering and Applied Science

Image interpolation using Shearlet based iterative refinement

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images

Tool for 3D analysis and segmentation of retinal layers in volumetric SD-OCT images

With the development of optical coherence tomography in the spectral domain (SD-OCT), it is now possible to quickly acquire large volumes of images. Typically analyzed by a specialist, the processing of the images is quite slow, consisting on the manual marking of features of interest in the retina, including the determination of the position and thickness of its different layers. This process is not consistent, the results are dependent on the clinician perception and do not take advantage of the technology, since the volumetric information that it currently provides is ignored. Therefore is of medical and technological interest to make a three-dimensional and automatic processing of images resulting from OCT technology. Only then we will be able to collect all the information that these images can give us and thus improve the diagnosis and early detection of eye pathologies. In addition to the 3D analysis, it is also important to develop visualization tools for the 3D data. This thesis proposes to apply 3D graphical processing methods to SD-OCT retinal images, in order to segment retinal layers. Also, to analyze the 3D retinal images and the segmentation results, a visualization interface that allows displaying images in 3D and from different perspectives is proposed. The work was based on the use of the Medical Imaging Interaction Toolkit (MITK), which includes other open-source toolkits. For this study a public database of SD-OCT retinal images will be used, containing about 360 volumetric images of healthy and pathological subjects. The software prototype allows the user to interact with the images, apply 3D filters for segmentation and noise reduction and render the volume. The detection of three surfaces of the retina is achieved through intensity-based edge detection methods with a mean error in the overall retina thickness of 3.72 0.3 pixels

Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images

Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI). Their primary goals are to enhance the imaging quality by synthesizing the desired modality and reducing the slice thickness. Despite the promising synthetic results, these techniques are often tailored to specific tasks, thereby limiting their adaptability to complex clinical scenarios. Therefore, it is crucial to build a unified network that can handle various image synthesis tasks with arbitrary requirements of modality and resolution settings, so that the resources for training and deploying the models can be greatly reduced. However, none of the previous works is capable of performing CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction methods often treat alias frequencies improperly, resulting in suboptimal detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free framework (Uni-COAL) to accomplish the aforementioned tasks with a single network. The co-modulation design of the image-conditioned and stochastic attribute representations ensures the consistency between CMS and SR, while simultaneously accommodating arbitrary combinations of input/output modalities and thickness. The generator of Uni-COAL is also designed to be alias-free based on the Shannon-Nyquist signal processing framework, ensuring effective suppression of alias frequencies. Additionally, we leverage the semantic prior of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic preservation of anatomical structures during synthesis. Experiments on three datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and CMSR tasks for MR images, which highlights its generalizability to wide-range applications