Marie Luong

Novel Example-Based Method for Super-Resolution and Denoising of Medical Images

In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.

Perceptual Watermarking Using Pyramidal JND Maps

A new pyramidal JND (just noticeable difference) model is presented. The idea is to use this JND to determine the optimum strength for embedding the watermark providing an invisible and robust watermarking scheme. The image is first decomposed into a multiresolution representation using the pyramidal decomposition. Then, a perceptual model is proposed to compute the JND value for each pixel at each Laplacian level. This model takes into account three main characteristics of the human visual system (HVS), namely: contrast sensitivity, luminance adaptation and contrast masking. The performance of the proposed technique is evaluated in terms of transparency, using subjective and objective tests, and robustness to different common attacks.

Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications

A new active contour (LGFGD) was developed in our earlier conference paper. This contour uses local and global information along with Gaussian distribution. The present paper derives the main LGFGD equation and investigates its parameters σ, λ and m. Specific values are determined (for σ, λ, m) to ensure high accuracy of segmentation of medical images containing nonhomogeneous and noisy regions with week boundaries. To validate the model, a new set of experiments was performed with new images including 24 skin lesion images with ground truth. Thus, a statistic of the LGFGD performance was calculated regarding the model’s interval of confidence. Comparison with contemporary methods from the field is provided as well.

Medical image denoising using Kernel Ridge Regression

Medical images are often corrupted by random noise, leading to undesirable visual quality. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this paper, we propose a novel learning method for the reduction of Gaussian noise of Computed Tomography (CT) image and Rician noise of Magnetic Resonance Imaging (MRI) image based on a given set of standard images and the Kernel Ridge Regression (KRR). Experimental results demonstrate the outperformance of the proposed technique over various other methods in terms of both objective and subjective evaluations.

A convex active contour region-based model for image segmentation

A novel region-based active contour model is proposed in this paper. By using the image local information in the energy function, our model is able to efficiently segment images with intensity inhomogeneity. Moreover, the proposed model is convex. So, it is independent of the initial condition. Furthermore, the energy function of the proposed model is minimized in a computationally efficient way by using the Chambolle method.

Combination of closest space and closest structure to ameliorate non-local means method

Recently non-local means (NLM) has been known to be one of the most attractive denoising algorithms. It alters each pixel by a weighted average of pixels in the image. The weights express the level of similarity between two small patches defined for two involved pixels. There are many propositions to ameliorate the performance of this method. One of branches is to seek the whole image the most similar patches for a given one. In this paper, we investigate this approach and show that it is suitable for only highly textured images. Moreover, we show that combination of this approach and the original NLM yields better result for all image types.

A perceptual pyramidal watermarking technique

This paper presents a new perceptual image watermarking scheme based on the Laplacian pyramid (LP) decomposition and a visibility map constructed using a human visual contrast model. This map is computed of each level of the pyramid in order to determine the auspicious regions for embedding the watermark. The spread spectrum technique is used to embed the watermark in some levels of the LP. The watermarked image is then constructed from the Laplacian images. The algorithm performances are evaluated in terms of watermark invisibility, using an objective image quality perceptual measure, the structural similarity index measure (SSIM), and robustness to different attacks of Stirmark such as Jpeg compression, low-pass filtering, additive noise and cropping.

Perceptual watermarking using a new Just-Noticeable-Difference model

In this paper, a new watermarking scheme based on Human Visual System (HVS) modeling is proposed. The approach consists in building computational models which take into account the most common properties of the HVS that can be exploited for watermarking. Two schemes for embedding and controlling the transparency of the watermark are presented, namely the implicit and the explicit schemes. Both schemes are designed in the framework of the pyramidal decomposition which has been shown to be a powerful tool for analyzing image through a multi-scale representation. For the first approach, a multi-scale visibility map is used to optimize the watermark embedding process. The second approach makes use of HVS properties in an explicit and more sophisticated manner that consists in tuning the watermark strength just beneath the visual detection threshold. A new JND (Just-Noticeable-Difference) model for determining this threshold is then proposed and evaluated. The obtained results provide a strong support for this new JND model.

An effective example-based learning method for denoising of medical images corrupted by heavy Gaussian noise and poisson noise

Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.

A new perceptually adaptive method for deblocking and deringing

In this paper, a new perceptually adaptive method for reducing the blocking and ringing artifacts encountered in image compression is proposed. The method consists of three steps: (i) blocking-ringing artifacts detection, (ii) perceptual distortion measure and (iii) blocking-ringing artifacts reduction. The performance of the proposed method is evaluated objectively and subjectively in terms of image fidelity and blocking, ringing and blur effects reduction. The obtained results are very promising and confirm once more the efficiency of perceptual approaches in image processing.