Image recovery from double amplitudes in fractional Fourier domain

The classical Gerchberg-Saxton algorithm is introduced into the image recovery in fractional Fourier domain after adaptation. When this algorithm is applied directly, its performance is good for smoothed image, but bad for unsmoothed image. Based on the diversity of fractional Fourier transform on its orders, this paper suggests a novel iterative algorithm, which extracts the information of the original image from amplitudes of its fractional Fourier transform at two orders. This new algorithm consists of two independent Gerchberg-Saxton procedures and an averaging operation in each circle. Numerical simulations are carried out to show its validity for both smoothed and unsmoothed images with most pairs of orders in the interval [0, 1].

SEMI-FRAGILE WATERMARKING ALGORITHM FOR IMAGE TAMPERS LOCALIZATION AND RECOVERY

Two watermarks are embedded into the original image. One is the authentication watermark generated by secret key, which is embedded into the sub-LSB （Least Significant Bit） of the original image for tamper localization; the other is the recovery watermark for tamper recovering. The original image is divided into 8 x 8 blocks and each block is transformed by Discrete Cosine Transform （DCT）. For each block, some lower frequency DCT coefficients are chosen to be quantized and binary encoded so as to gain the recovery watermark of each block, and the recovery watermark is embedded into the LSB of another block by chaos encryption and authentication chain technology. After the two watermarks being detected, the location of any minute changes in image can be detected, and the tampered image data can be recovered effectively. In the paper, the number of coefficients and their bit lengths are carefully chosen in order to satisfy with the payload of each block and gain the capability of self-recovering. The proposed algorithm can well resist against possible forged attacks. Experimental results show that the watermark generated by the proposed algorithm is sensitive to tiny changes in images, and it has higher accuracy of tamper localization and good capability of the tamper recovery.

Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery

Compressive sensing theory mainly includes the sparsely of signal processing,the structure of the measurement matrix and reconstruction algorithm.Reconstruction algorithm is the core content of CS theory,that is,through the low dimensional sparse signal recovers the original signal accurately.This thesis based on the theory of CS to study further on seismic data reconstruction algorithm.We select orthogonal matching pursuit algorithm as a base reconstruction algorithm.Then do the specific research for the implementation principle,the structure of the algorithm of AOMP and make the signal simulation at the same time.In view of the OMP algorithm reconstruction speed is slow and the problems need to be a given number of iterations,which developed an improved scheme.We combine the optimized OMP algorithm of constraint the optimal matching of item selection strategy,the backwards gradient projection ideas of adaptive variance step gradient projection method and the original algorithm to improve it.Simulation experiments show that improved OMP algorithm is superior to traditional OMP algorithm of improvement in the reconstruction time and effect under the same condition.This paper introduces CS and most mature compressive sensing algorithm at present orthogonal matching pursuit algorithm.Through the program design realize basic orthogonal matching pursuit algorithms,and design realize basic orthogonal matching pursuit algorithm of one-dimensional,two-dimensional signal processing simulation.

Over exposed image information recovery via stochastic resonance

Over exposure is rather annoying in photo taking. However, in some severe light conditions over exposure is inevitable using conventional cameras due to the limitation of dynamic range of the image sensor. The over exposed information would be completely lost and unrecoverable. In order to cope with this problem, we propose a novel technique in which the noise is used to enlarge the dynamic range of the image sensor. The essential mechanism that noise contributes to the information recovery is investigated. It is also proved that the visibility of regained information can reach the peak when specifically added noise is synchronized with the image sensor, thus activating the phenomenon of stochastic resonance （SR）. Four different types of noises are investigated to show the effects of variant distributions on the quality of recovered information. The experimental outcomes are consistent with our theoretical results, which indicates that the SR-based lost information recovery is quite promising.

Early Diagnosis of Recurrent Optic Neuritis Using Contrast-Enhanced T2 Fluid-attenuated Inversion Recovery Imaging：a Case Report

The diagnosis of the recurrent optic neuritis is commonly established clinically,and sometimes it could be challenging because the involved optic nerve does not always show significant enhancement on conventional contrast enhanced-T1 weighted imaging（CE-T1W1）.In this paper,we reported a middle-aged female with early diagnosis of recurrent optic neuritis using contrast-enhanced T2 fluid-attenuated inversion recovery imaging（CET2FLAIR）.The involved optic nerve presented evident enhancement on CE-T2FLAIR and no enhancement on CE-T1W1.This case suggested that the CE-T2FLAIR may be a useful diagnostic tool specifically for the recurrent optic neuritis in clinical practice.

Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on block classification and permutation is proposed.Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively.After block classification,content owner utilizes a specific encryption method,including stream cipher encryption and block permutation to protect image content securely.For the encrypted image,data hider embeds additional secret information in the most significant bits(MSB)of the encrypted pixels in smooth blocks and the final marked image can be obtained.At the receiver side,secret data will be extracted correctly with data-hiding key.When receiver only has encryption key,after stream cipher decryption,block scrambling decryption and MSB error prediction with threshold,decrypted image will be achieved.When data hiding key and encryption key are both obtained,receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly,hence,receiver can recover marked image losslessly.Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.

High-payload completely reversible data hiding in encrypted images by an interpolation technique

We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits （MSBs） of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

Optimization of Demosaicing Algorithm for Autofluorescence Imaging System

Autofluorescence imaging(AFI) systems are widely used in the detection of precancerous lesions.Fluorescence images of precancerous tissue are usually red(R) or blue(B), so this kind of system has high requirement for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission with no time delay. Existing colour recovery algorithms focus more on green(G) channel, overlooking R and B channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the correlation between colour channels. The experimental results show that the peak signal-to-noise ratios(PSNRs)of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the existing demosaicing algorithms for AFI system.