Sharif Digital Repository

Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain. However, clinical acquisitions are often low resolution. This paper proposes a method for improving the resolution using sparse representation. In this method a non-diffusion weighted image (bO) is utilized to learn the patches and then diffusion weighted images are reconstructed based on the trained dictionary. Our method is compared with bilinear, nearest neighbor and bicubic interpolation methods. The proposed method shows improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM)  

This article introduces a new adaptive method for image interpolation. In order to obtain a high resolution (HR) image from its low resolution (LR) counterpart (original image), an interpolator function (array) is used, and the main focus of this manuscript is to formulate and define this function. By applying this interpolator function to each row and column of a LR image, it is possible to construct its HR counterpart. One of the main challenges of image interpolation algorithms is to maintain the edge structures while developing an HR image from the LR replica. The proposed approach overcomes this challenge and exhibits remarkable results at the image edges. The peak signal to noise ratio... 

The authors address the use of sparse representation to securely hide a message within non-overlapping blocks of a given colour image in the wavelet domain. All four sub-images of the two-dimensional wavelet transform of two colour bands are used for data embedding without affecting the image perceptibility. Bit error rate of hidden data extraction is reduced to zero by introducing a novel refinement procedure in the proposed algorithm. The refinement procedure introduced solves the hidden bit extraction errors caused by the rounding process, the overflows and the nature of approximation in sparse decomposition. Capacity of the proposed method is calculated using necessary conditions for... 

In image hiding we embed a secret image into a cover image. In order to minimize the distortion of secret image, we presented a novel steganographic method based on content-aware seam carving. In this paper, we propose a new image hiding method in which, the secret image is initially classified based on image complexity measure. Then it is resized to an appropriate smaller size, but for having the important objects of the image in resized image we use seam carving method that resizes an image whereas the important content of it remains. By applying the seam carving, we have an image that is smaller than the original one, and then we hide it in a cover image. Obviously hiding a smaller secret... 

Compressed Sensing (CS), as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this paper, compressed sensing method is proposed to reduce the noise and reconstruct the image signal. Noise reduction and image reconstruction are formulated in the theoretical framework of... 

A novel CT imaging structure based on Compressive Sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and, thus, X-ray radiation dosage without compromising the image quality. The utilized compressive sensing approach is based on radial Fourier sampling. Thanks to the intrinsic relation between captured radon samples in a CT imaging process and the radial Fourier samples, partial Fourier sampling could be implemented systematically. This systematic compressive sampling helps in better control of required conditions such as incoherence and sparsity to guarantee adequate image quality in comparison to previous CS-based CT imaging structures. Simulation results prove the... 

This paper focuses on an efficient approach for designing multi-static arrays for millimeter-wave imaging, based on the k -space or Fourier-spatial domain characteristic of imaging systems. Our goal is to decrease the redundancy of the data measured by each antenna and to improve the resolution of the reconstructed image. The proposed technique is based on determining the role of each transmitter and receiver, in collecting the data from each voxel of the target in k -space domain and then rotating the transmitters' beams to measure the desirable information. The effect of non-uniform redundant k -space domain frequency samples that act as an undesirable filter is compensated using a... 

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.... 

This paper presents an efficient scheme for blind watermark attacking using the concept of matching of the long-range data. The main idea of the proposed attack is to add plenty of noise to the watermarked image and then try to restore an unwatermarked copy of the noisy image. The aim is to destroy the watermark information without accessing the parameters used during the watermark embedding process. So, it allows our approach to be completely free from any pre-assumption on the watermarking algorithm or any other parameters that is used during the watermark embedding procedure. Experimental results show the proposed algorithm's superiority over several other traditional watermarking... 

Along with the improvement of image watermarking techniques, the necessity for effectively and comprehensively evaluating various algorithms becomes imperative. In this paper, we first propose a new categorization that fits for most of the existing watermarking algorithms that work in the wavelet domain. Then an adaptive watermarking attack for evaluating the robustness of watermarking schemes that are based on the proposed categorization is presented. This attack determines the flat regions, edges, and textures of the watermarked image and based on known features of each region the proposed attack tries to destroy the watermark information. This is done by separately manipulating the... 

Denoising of corrupted images has been a classical problem in image processing. In this paper we propose a new approach for image noise reduction using wavelet transform. In this method an improved version of thresholding neural networks (TNN) is used to find the optimum threshold values in the sense of minimum mean square error (MMSE). Based on these optimum thresholds a novel cycle-spinning based method is used to reduce image artifacts. In this method, we utilize two thresholding schemes as the thresholding operator of cycle-spinning. A neighbor dependent thresholding scheme is employed as its first shrinkage step and a simple wavelet thresholding with the optimum derived threshold values... 

In recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated... 

The emerging multimedia applications of Wireless Sensor Network (WSNs) impose new challenges in design of algorithms and communication protocols for such networks. In the view of these challenges, error control is an important mechanism that enables us to provide robust multimedia communication and maintain Quality of Service (QoS). Despite the existence of some good research works on error control analysis in WSNs, none of them provides a thorough study of error control schemes for multimedia delivery. In this paper, a comprehensive performance evaluation of Automatic Repeat Request (ARQ), Forward Error Correction (FEC), Erasure Coding (EC), link-layer hybrid FEC/ARQ, and cross-layer hybrid... 

In this paper, a discrete cosine transform (DCT) based blind watermarking scheme based on spread spectrum communications is proposed. We perform block-based DCT (BDCT) on the host image; then using the DC coefficients of each block, we construct a low-resolution approximation image. We apply BDCT on this approximation image, then watermark is embedded by adding a pseudo random noise sequence into its high frequencies. In detection stage, we extract the approximation image from the watermarked image, then the same pseudo random noise sequence is generated, and its correlation is computed with high frequencies of the watermarked approximation image. In our method, higher robustness is obtained... 

Purpose: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. Methods: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the...