Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise

Diffusion-Steered Super-Resolution Image Reconstruction

For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. Despite the advantages, super-resolution suffers from ill-posedness, a problem that makes the technique susceptible to multiple solutions. Therefore, scholars have proposed regularization approaches as attempts to address the challenge. The present work introduces a parameterized diffusion-steered regularization framework that integrates total variation (TV) and Perona-Malik (PM) smoothing functionals into the classical super-resolution model. The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate reliable and appreciable results. Extensive analysis of the proposed resolution-enhancement model shows that it can respond well on different image regions. Experimental results provide further evidence that the proposed model outperforms

7T-guided super-resolution of 3T MRI

High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper

Reconstruction of 7T-Like Images From 3T MRI

In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images

Depth-Map-Assisted Texture and Depth Map Super-Resolution

With the development of video technology, high definition video and 3D video applications are becoming increasingly accessible to customers. The interactive and vivid 3D video experience of realistic scenes relies greatly on the amount and quality of the texture and depth map data. However, due to the limitations of video capturing hardware and transmission bandwidth, transmitted video has to be compressed which degrades, in general, the received video quality. This means that it is hard to meet the users’ requirements of high definition and visual experience; it also limits development of future applications. Therefore, image/video super-resolution techniques have been proposed to address this issue. Image super-resolution aims to reconstruct a high resolution image from single or multiple low resolution images captured of the same scene under different conditions. Based on the image type that needs to be super-resolved, image super-resolution includes texture and depth image super-resolutions. If classified based on the implementation methods, there are three main categories: interpolation-based, reconstruction-based and learning-based super-resolution algorithms. This thesis focuses on exploiting depth data in interpolation-based super-resolution algorithms for texture video and depth maps. Two novel texture and one depth super-resolution algorithms are proposed as the main contributions of this thesis. The first texture super-resolution algorithm is carried out in the Mixed Resolution (MR) multiview video system where at least one of the views is captured at Low Resolution (LR), while the others are captured at Full Resolution (FR). In order to reduce visual uncomfortableness and adapt MR video format for free-viewpoint television, the low resolution views are super-resolved to the target full resolution by the proposed virtual view assisted super resolution algorithm. The inter-view similarity is used to determine whether to fill the missing pixels in the super-resolved frame by virtual view pixels or by spatial interpolated pixels. The decision mechanism is steered by the texture characteristics of the neighbors of each missing pixel. Thus, the proposed method can recover the details in regions with edges while maintaining good quality at smooth areas by properly exploiting the high quality virtual view pixels and the directional correlation of pixels. The second texture super-resolution algorithm is based on the Multiview Video plus Depth (MVD) system, which consists of textures and the associated per-pixel depth data. In order to further reduce the transmitted data and the quality degradation of received video, a systematical framework to downsample the original MVD data and later on to super-resolved the LR views is proposed. At the encoder side, the rows of the two adjacent views are downsampled following an interlacing and complementary fashion, whereas, at the decoder side, the discarded pixels are recovered by fusing the virtual view pixels with the directional interpolated pixels from the complementary downsampled views. Consequently, with the assistance of virtual views, the proposed approach can effectively achieve these two goals. From previous two works, we can observe that depth data has big potential to be used in 3D video enhancement. However, due to the low spatial resolution of Time-of-Flight (ToF) depth camera generated depth images, their applications have been limited. Hence, in the last contribution of this thesis, a planar-surface-based depth map super-resolution approach is presented, which interpolates depth images by exploiting the equation of each detected planar surface. Both quantitative and qualitative experimental results demonstrate the effectiveness and robustness of the proposed approach over benchmark methods