Copyright Protection of Color Imaging Using Robust-Encoded Watermarking

In this paper we present a robust-encoded watermarking method applied to color images for copyright protection, which presents robustness against several geometric and signal processing distortions. Trade-off between payload, robustness and imperceptibility is a very important aspect which has to be considered when a watermark algorithm is designed. In our proposed scheme, previously to be embedded into the image, the watermark signal is encoded using a convolutional encoder, which can perform forward error correction achieving better robustness performance. Then, the embedding process is carried out through the discrete cosine transform domain (DCT) of an image using the image normalization technique to accomplish robustness against geometric and signal processing distortions. The embedded watermark coded bits are extracted and decoded using the Viterbi algorithm. In order to determine the presence or absence of the watermark into the image we compute the bit error rate (BER) between the recovered and the original watermark data sequence. The quality of the watermarked image is measured using the well-known indices: Peak Signal to Noise Ratio (PSNR), Visual Information Fidelity (VIF) and Structural Similarity Index (SSIM). The color difference between the watermarked and original images is obtained by using the Normalized Color Difference (NCD) measure. The experimental results show that the proposed method provides good performance in terms of imperceptibility and robustness. The comparison among the proposed and previously reported methods based on different techniques is also provided

ShearLab 3D: Faithful Digital Shearlet Transforms based on Compactly Supported Shearlets

Wavelets and their associated transforms are highly efficient when approximating and analyzing one-dimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient of sparsely approximating and also of analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional representation system extending the wavelet framework, which overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful implementation and fast associated transforms. In this paper, we will introduce a comprehensive carefully documented software package coined ShearLab 3D (www.ShearLab.org) and discuss its algorithmic details. This package provides MATLAB code for a novel faithful algorithmic realization of the 2D and 3D shearlet transform (and their inverses) associated with compactly supported universal shearlet systems incorporating the option of using CUDA. We will present extensive numerical experiments in 2D and 3D concerning denoising, inpainting, and feature extraction, comparing the performance of ShearLab 3D with similar transform-based algorithms such as curvelets, contourlets, or surfacelets. In the spirit of reproducible reseaerch, all scripts are accessible on www.ShearLab.org.Comment: There is another shearlet software package (http://www.mathematik.uni-kl.de/imagepro/members/haeuser/ffst/) by S. H\"auser and G. Steidl. We will include this in a revisio

Modified CSLBP

Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations

Ownership protection of plenoptic images by robust and reversible watermarking

Plenoptic images are highly demanded for 3D representation of broad scenes. Contrary to the images captured by conventional cameras, plenoptic images carry a considerable amount of angular information, which is very appealing for 3D reconstruction and display of the scene. Plenoptic images are gaining increasing importance in areas like medical imaging, manufacturing control, metrology, or even entertainment business. Thus, the adaptation and refinement of watermarking techniques to plenoptic images is a matter of raising interest. In this paper a new method for plenoptic image watermarking is proposed. A secret key is used to specify the location of logo insertion. Employing discrete cosine transform (DCT) and singular value decomposition (SVD), a robust feature is extracted to carry the watermark. The Peak Signal to Noise Ratio (PSNR) of the watermarked image is always higher than 54.75 dB which is by far more than enough for Human Visual System (HVS) to discriminate the watermarked image. The proposed method is fully reversible and, if no attack occurs, the embedded logo can be extracted perfectly even with the lowest figures of watermark strength. Even if enormous attacks occur, such as Gaussian noise, JPEG compression and median filtering, our method exhibits significant robustness, demonstrated by promising bit error rate (BER) performance

Single frame super-resolution image system

The estimation of some unknown quantity information from known observable information can be viewed as a specific statistical process which needs an extra source of information prediction strategy. In this regard, image super-resolution is an important application In this thesis, we proposed a new image interpolation method based on Redundant Discrete Wavelet Transform (RDWT) and self-adaptive processes in which edge direction details are considered to solve single-frame image super-resolution task. Information about sharp variations, both in horizontal and vertical directions derived from wavelet transform sub-bands are considered, followed by detection and modification of the aliasing part in the preliminary output in order to increase the visual effect. By exploiting fundamental properties of images such as property of edge direction, different parts of the source image are considered separately in order to predict the vertical and horizontal details accurately, helping to consummate the whole framework in reconstructing the high-resolution image. Extensive tests of the proposed method show that both objective quality (PSNR) and subjective quality are obviously improved compared to several other state-of-the-art methods. And this work also leaved capacious space for further research, not only theoretical but also practical. Some of the related research applications based on this algorithm strategy are also briefly introduced

A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity

The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping "pictures". We hope that this paper will contribute to the appreciation and apprehension of a stream of current research directions in image understanding.Comment: 65 pages, 33 figures, 303 reference

Medical image processing: applications in ophthalmology and total hip replacement

Medical imaging tools technologically supported by the recent advances in the areas of computer vision can provide systems that aid medical professionals to carry out their expert diagnostics and investigations more effectively and efficiently. Two medical application domains that can benefit by such tools are ophthalmology and Total Hip Replacement (THR). Although a literature review conducted within the research context of this thesis revealed a number of existing solutions these are either very much limited by their application scope, robustness or scope of the extensiveness of the functionality made available. Therefore this thesis focuses on initially investigating a number of requirements defined by leading experts in the respective specialisms and providing practical solutions, well supported by the theoretical advances of computer vision and pattern recognition. This thesis provides three novel algorithms/systems for use within image analysis in the areas of Ophthalmology and THR. The first approach uses Contourlet Transform to analyse and quantify corneal neovascularization. Experimental results are provided to prove that the proposed approach provides improved robustness in the presence of noise, non-uniform illumination and reflections, common problems that exist in captured corneal images. The second approach uses a colour based segmentation approach to segment, measure and analyse corneal ulcers using the HVS colour space. Literature review conducted within the research context of this thesis revealed that there is no such system available for analysis and measurement of corneal ulcers. Finally the thesis provides a robust approach towards detecting and analysing possible dislocations and misalignments in THR X-ray images. The algorithm uses localised histogram equalisation to enhance the quality of X-ray images first prior to using Hough Transforms and filtered back projections to locate and recognise key points of the THR x-ray images. These key points are then used to measure the possible presence of dislocations and misalignments. The thesis further highlights possible extensions and improvements to the proposed algorithms and systems