Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Semi-Sparsity for Smoothing Filters

In this paper, we propose an interesting semi-sparsity smoothing algorithm based on a novel sparsity-inducing optimization framework. This method is derived from the multiple observations, that is, semi-sparsity prior knowledge is more universally applicable, especially in areas where sparsity is not fully admitted, such as polynomial-smoothing surfaces. We illustrate that this semi-sparsity can be identified into a generalized $L_0$-norm minimization in higher-order gradient domains, thereby giving rise to a new "feature-aware" filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces). Notice that a direct solver is always unavailable due to the non-convexity and combinatorial nature of $L_0$-norm minimization. Instead, we solve the model based on an efficient half-quadratic splitting minimization with fast Fourier transforms (FFTs) for acceleration. We finally demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications

Temporally Coherent Video Stylization

International audienceThe transformation of video clips into stylized animations remains an active research topic in Computer Graphics. A key challenge is to reproduce the look of traditional artistic styles whilst minimizing distracting flickering and sliding artifacts; i.e. with temporal coherence. This chapter surveys the spectrum of available video stylization techniques, focusing on algorithms encouraging the temporally coherent placement of rendering marks, and discusses the trade-offs necessary to achieve coherence. We begin with flow-based adaptations of stroke based rendering (SBR) and texture advection capable of painting video. We then chart the development of the field, and its fusion with Computer Vision, to deliver coherent mid-level scene representations. These representations enable the rotoscoping of rendering marks on to temporally coherent video regions, enhancing the diversity and temporal coherence of stylization. In discussing coherence, we formalize the problem of temporal coherence in terms of three defined criteria, and compare and contrast video stylization using these

Image editing and interaction tools for visual expression

Digital photography is becoming extremely common in our daily life. However, images are difficult to edit and interact with. From a user's perspective, it is important to interact freely with the images on his/her smartphone or ipad. In this thesis we develop several image editing and interaction systems with this idea in mind. We aim for creating visual models with pre-computed internal structures such that interaction is readily supported. We demonstrate that such interactable models, driven by a user's hand, can render powerful visual expressiveness, and make static pixel arrays much more fun to play with. The first system harnesses the editing power of vector graphics. We convert raster images into a vector representation using Loop's subdivision surfaces. An image is represented by a multi-resolution feature-preserving sparse control mesh, with which image editing can be done at semantic level. A user can easily put a smile on a face image, or adjust the level of scene abstractness through a simple slider. The second system allows one to insert an object from image into a new scene. The key is to correct the shading on the object such that it goes consistently with the scene. Unlike traditional approach, we use a simple shape to capture gross shading effects and a set of shading detail images to account for visual complexities. The high-frequency nature of these detail images allows a moderate range of interactive composition effects without causing alarming visual artifacts. The third system is on video clips instead of a single image. We proposed a fully automated algorithm to creat

Artistic rendering enhancing global structure

Non-photorealistic rendering techniques usu- ally produce abstracted images. Most existing methods consider local rendering primitives, and global struc- tures may be easily obscured. Inspired by artists, we propose a novel image abstraction method that con- siders preserving or even enhancing global structures in the input images. Linear structures are particularly considered due to their wide existence and the avail- ability of techniques for their reliable detection. Based on various computer vision techniques, the algorithm is fully automatic. As demonstrated in the paper, artistic looking results are obtained for various types of images. The technique is orthogonal to many non-photorealistic rendering techniques and can be combined with them