De-emphasis of distracting image regions using texture power maps

We present a post-processing technique that selectively reduces the salience of distracting regions in an image. Computational models of attention predict that texture variation influences bottom-up attention mechanisms. Our method reduces the spatial variation of texture using power maps, high-order features describing local frequency content in an image. Modification of power maps results in effective regional de-emphasis. We validate our results quantitatively via a human subject search experiment and qualitatively with eye tracking data.Singapore-MIT Alliance (SMA

Style transfer for headshot portraits

Headshot portraits are a popular subject in photography but to achieve a compelling visual style requires advanced skills that a casual photographer will not have. Further, algorithms that automate or assist the stylization of generic photographs do not perform well on headshots due to the feature-specific, local retouching that a professional photographer typically applies to generate such portraits. We introduce a technique to transfer the style of an example headshot photo onto a new one. This can allow one to easily reproduce the look of renowned artists. At the core of our approach is a new multiscale technique to robustly transfer the local statistics of an example portrait onto a new one. This technique matches properties such as the local contrast and the overall lighting direction while being tolerant to the unavoidable differences between the faces of two different people. Additionally, because artists sometimes produce entire headshot collections in a common style, we show how to automatically find a good example to use as a reference for a given portrait, enabling style transfer without the user having to search for a suitable example for each input. We demonstrate our approach on data taken in a controlled environment as well as on a large set of photos downloaded from the Internet. We show that we can successfully handle styles by a variety of different artists.Quanta Computer (Firm)Adobe System

Enhancement and stylization of photographs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-95).A photograph captured by a digital camera may be the final product for many casual photographers. However, for professional photographers, this photograph is only the beginning: experts often spend hours on enhancing and stylizing their photographs. These enhancements range from basic exposure and contrast adjustments to dramatic alterations. It is these enhancements - along with composition and timing - that distinguish the work of professionals and casual photographers. The goal of this thesis is to narrow the gap between casual and professional photographers. We aim to empower casual users with methods for making their photographs look better. Professional photographers could also benefit from our findings: our enhancement methods produce a better starting point for professional processing. We propose and evaluate three different methods for image enhancement and stylization. First method is based on photographic intuition and is fully automatic. The second method relies on expert's input for training; after the training this method can be used to automatically predict expert adjustments for previously unseen photographs. The third method uses a grammar-based representation to sample the space of image filter and relies on user input to select novel and interesting filters.by Vladimir Leonid Bychkovsky.Ph.D

영상 복원 문제의 변분법적 접근

학위논문 (박사)-- 서울대학교 대학원 : 수리과학부, 2013. 2. 강명주.Image restoration has been an active research area in image processing and computer vision during the past several decades. We explore variational partial differential equations (PDE) models in image restoration problem. We start our discussion by reviewing classical models, by which the works of this dissertation are highly motivated. The content of the dissertation is divided into two main subjects. First topic is on image denoising, where we propose non-convex hybrid total variation model, and then we apply iterative reweighted algorithm to solve the proposed model. Second topic is on image decomposition, in which we separate an image into structural component and oscillatory component using local gradient constraint.Abstract i 1 Introduction 1 1.1 Image restoration 2 1.2 Brief overview of the dissertation 3 2 Previous works 4 2.1 Image denoising 4 2.1.1 Fundamental model 4 2.1.2 Higher order model 7 2.1.3 Hybrid model 9 2.1.4 Non-convex model 12 2.2 Image decomposition 22 2.2.1 Meyers model 23 2.2.2 Nonlinear filter 24 3 Non-convex hybrid TV for image denoising 28 3.1 Variational model with non-convex hybrid TV 29 3.1.1 Non-convex TV model and non-convex HOTV model 29 3.1.2 The Proposed model: Non-convex hybrid TV model 31 3.2 Iterative reweighted hybrid Total Variation algorithm 33 3.3 Numerical experiments 35 3.3.1 Parameter values 37 3.3.2 Comparison between the non-convex TV model and the non-convex HOTV model 38 3.3.3 Comparison with other non-convex higher order regularizers 40 3.3.4 Comparison between two non-convex hybrid TV models 42 3.3.5 Comparison with Krishnan et al. [39] 43 3.3.6 Comparison with state-of-the-art 44 4 Image decomposition 59 4.1 Local gradient constraint 61 4.1.1 Texture estimator 62 4.2 The proposed model 65 4.2.1 Algorithm : Anisotropic TV-L2 67 4.2.2 Algorithm : Isotropic TV-L2 69 4.2.3 Algorithm : Isotropic TV-L1 71 4.3 Numerical experiments and discussion 72 5 Conclusion and future works 80 Abstract (in Korean) 92Docto

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information

Improving visual search performance in augmented reality environments using a subtle cueing approach: Experimental methods, apparatus development and evaluation.

Ph.DDOCTOR OF PHILOSOPH

Saliency-based image enhancement

Ph.DDOCTOR OF PHILOSOPH