Watermarking-Based Inpainting Under Data Transmition Environment

[[abstract]]This studyb proposes a novel image inpainting technique based on watermarking and halftoning. This technique use LSB method to embed error diffusion halftone image into original image for protecting the image. In image repair process, we use LSB method to extract the halftone information, and the reference image is achieved from LUT inverse halftone. Finally we use the reference imageto finish the image inpainting work. Experiment shows the performance of our method is very excellent in image inpainting.[[conferencetype]]國際[[conferencedate]]20101206~20101208[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Chengdu, Chin

Компресія зображень апроксимацією поліноміальними функціями їх дискретного косинусного перетворення

Methods and algorithms   of image approximation using polynomial functions are considered. Compression and accuracy characteristics of image by discrete cosine transform are investigatedРассмотрены методы и способы аппроксимации изображения с использованием полиномиальных функций. Исследовано характеристики компрессии и точности изображений с применением дискретного косинусного превращенияРозглянуто методи та способи апроксимації зображення з використанням поліноміальних функцій. Досліджено характеристики компресії та точності апроксимованих зображень із застосуванням дискретного косинусного перетворенн

Learning Hierarchical and Topographic Dictionaries with Structured Sparsity

Recent work in signal processing and statistics have focused on defining new regularization functions, which not only induce sparsity of the solution, but also take into account the structure of the problem. We present in this paper a class of convex penalties introduced in the machine learning community, which take the form of a sum of l_2 and l_infinity-norms over groups of variables. They extend the classical group-sparsity regularization in the sense that the groups possibly overlap, allowing more flexibility in the group design. We review efficient optimization methods to deal with the corresponding inverse problems, and their application to the problem of learning dictionaries of natural image patches: On the one hand, dictionary learning has indeed proven effective for various signal processing tasks. On the other hand, structured sparsity provides a natural framework for modeling dependencies between dictionary elements. We thus consider a structured sparse regularization to learn dictionaries embedded in a particular structure, for instance a tree or a two-dimensional grid. In the latter case, the results we obtain are similar to the dictionaries produced by topographic independent component analysis

Learning the Morphological Diversity

International audienceThis article proposes a new method for image separation into a linear combination of morphological components. Sparsity in global dictionaries is used to extract the cartoon and oscillating content of the image. Complicated texture patterns are extracted by learning adapted local dictionaries that sparsify patches in the image. These global and local sparsity priors together with the data fidelity define a non-convex energy and the separation is obtained as a stationary point of this energy. This variational optimization is extended to solve more general inverse problems such as inpainting. A new adaptive morphological component analysis algorithm is derived to find a stationary point of the energy. Using adapted dictionaries learned from data allows to circumvent some difficulties faced by fixed dictionaries. Numerical results demonstrate that this adaptivity is indeed crucial to capture complex texture patterns