Sharif Digital Repository

Reflection appears as a layer that partly covers the original image. This phenomenon may cause failure in extracting information from images of reflecting objects. This work presents an automated technique for determining of reflection area as well as its severity to define reliability of extracted information. This is done by analyzing histogram and objects found in the image. We present a strong detector based on combining the results of these two procedures. We presented new reflection detection algorithm and a new method to find a good threshold value for converting any image into a binary image. The average reflection detection accuracy of the proposed algorithm is more than 95% for... 

Recently, protecting the copyright of digital media has become an imperative issue due to the growing illegal reproduction and modification of digital media. A large number of digital watermarking algorithms have been proposed to protect the integrity and copyright of images. Traditional watermarking schemes protect image copyright by embedding a watermark in the spatial or frequency domain of an image. However, these methods degrade the quality of the original image in some extend. In recent years, a new approach called zero-watermarking algorithms is introduced. In these methods, the watermark does not require to be embedded into the protected image but is used to generate a verification... 

This paper proposes an image watermarking scheme for copyright protection based on Independent Component Analysis (ICA). In the suggested scheme, embedding is carried out in cumulative form in spatial domain and ICA is used for watermark extraction. For extraction there is no need to access the original image or the watermark, and extraction is carried out only with two watermarked images. Experimental results show that the new method has better quality than famous methods [1], [2], [3] in spatial or frequency domain and is robust against various attacks. Noise addition, resizing, low pass filtering, multiple marks, gray-scale reduction, rotation, JPEG compression, and cropping are some... 

In this paper, we propose a new method for impulsive noise removal from images. It uses the sparsity of natural images when they are expanded by mean of a good learned dictionary. The zeros in sparse domain give us an idea to reconstruct the pixels that are corrupted by random-value impulse noises. This idea comes from this reality that noisy image in sparse domain of original image will not have a sparse representation as much as original image sparsity. In this method we assume that the proper dictionary to achieve image in sparse domain is available  

This paper proposes an image watermarking scheme for copyright protection based on Independent Component Analysis (ICA). In the suggested scheme, we divide the original image into blocks and use the ICA to project the image into a basis with its components as statistically independent as possible. The data embedding is carried out in cumulative form in the ICA basis and also the ICA is used for watermark extraction. The extraction process could be either non-blind, through a straightforward procedure, or blind, via a tricky method we propose. Experimental results show that the new method outperforms some well-known image watermarking methods [1], [2], [3] in spatial or frequency domain and... 

A new high capacity method for transform domain image steganography is introduced in this paper. The proposed steganography algorithm works on the wavelet transform coefficients of the original image to embed the secret data. As compared to current transform domain data hiding methods, this scheme can provide a larger capacity for data hiding without sacrificing the cover image quality. This is achieved through retaining integrity of the wavelet coefficients at high capacity embedding. This improvement to capacity-quality trading-off interrelation is analyzed in detailed and experimentally illustrated in the paper  

In this paper we propose a new method to recover images corrupted with salt and pepper noise. The proposed method uses ordered statistics filtering and soft-decisioning in successive approximations to find the locations and amplitudes of all the impulses. In our method we use soft-decision to generate a mask which is based on histogram distributions in local windows to distinguish impulsive noise from legitimate pixels. This mask is used to obtain an estimate of the original image by an iterative method. The result of the aforementioned algorithm can be fed back into the system to improve the image reconstruction process. This algorithm is analyzed and verified by simulation results.... 

Synthetic aperture radar (SAR) appropriate image processing in conjunction with noise reduction is crucial in proper image segmentation. The authors present a new algorithm, logarithmic adaptive soft morphological (LASM) filter, utilising collectivity and flexibility of order-statistic soft morphological filters. This method not only reduces the speckle noise of the single-look SAR imagery considerably, but it significantly enhances the segmentation results. To verify the performance, a simulated SAR image is first created by applying an imagery method to an original noiseless image. The resulting image has characteristics identical to a real SAR image. The LASM method, as well as several... 

In this paper, we present a novel multilinear algebra based feature extraction approach for face recognition which preserves some implicit structural or locally-spatial information among elements of the original images. We call this method three-dimensional modular discriminant analysis (3DMDA). Our approach uses a new data model called third-order tensor model (3TM) for representing the face images. In this model, each image is partitioned into the several equal size local blocks, and the local blocks are combined to represent the image as a third-order tensor. Then, a new optimization algorithm called direct mode (d-mode) is introduced for learning three optimal projection axes. Extensive... 

Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions. This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model. In addition, we show that two-dimensional Linear Discriminant... 

This paper proposes a new approach to watermarking multimedia products based on the combination of redundant discrete wavelet transform and independent component analysis. The original image is decomposed by RDWT, and watermark is embedded in to LL sub-band frequency according mixing model of the ICA, after that for enhancing the robustness of the watermark, the perceptual model is applied via stochastic approach for watermark adapting. This is based on computation of a noise visibility function (NVF) which has local image properties so the strength of watermarking is controllable. Principal component analysis (PCA) whitening process and FastICA techniques are introduced to ensure a blind...