White Noise Images Research Articles

Optical Asymmetric Cryptosystem Based on Dynamic Foveated Imaging and Bidimensional Empirical Mode Decomposition

In this paper, we propose an asymmetric cryptosystem based on dynamic foveated imaging and bidimensional empirical mode decomposition (BEMD). Firstly, a novel dynamic foveated imaging algorithm is developed to transform a plaintext image to a globally ambiguous and locally clear image. Then, the image is passed through a phase-truncated Fourier transform system to generate a white noise image. The resulting image is encoded using BEMD to produce an encrypted image. The proposed cryptosystem offers two distinct decryption methods, allowing the receiver to obtain a decrypted image from a specific frame or a combination of frames, depending on the unique keys. This encryption scheme significantly expands the key space and strengthens the system’s anti-iterative attack capability. Numerical simulation results demonstrate the effectiveness, security and robustness of the proposed cryptosystem.

Research on the development strategy of art industry based on RNN model

Abstract In this paper, we firstly propose an art style classification system based on the RNN model to study the development of the art industry, and continuously fine-tune this white noise image according to the long and short-term memory network and gated recurrent unit network until it makes this white noise image similar to the artwork image style, and the ink painting image feature extraction is mainly studied from the recurrent neural network based ink style feature extraction and the overall nested edge based The two aspects of brushstroke feature extraction based on overall nested edge detection are studied. Then, based on the art industry and big data, the ink painting style classification system is constructed, and the effectiveness of the system is verified by model art classification effect analysis. The results show that the RNN performs well on the face dataset, where the accuracy of multiple classifications up to 10 categories is above 85%. This indicates that the RNN model in this paper can maintain good performance in the ink painting art classification task.

Minimum description length clustering to measure meaningful image complexity

We present a new image complexity metric. Existing complexity metrics cannot distinguish meaningful content from noise, and give a high score to white noise images, which contain no meaningful information. We use the minimum description length principle to determine the number of clusters and designate certain points as outliers and, hence, correctly assign white noise a low score. The presented method is a step towards humans’ ability to detect when data contain a meaningful pattern. It also has similarities to theoretical ideas for measuring meaningful complexity. We conduct experiments on seven different sets of images, which show that our method assigns the most accurate scores to all images considered. Additionally, comparing the different levels of the hierarchy of clusters can reveal how complexity manifests at different scales, from local detail to global structure. We then present ablation studies showing the contribution of the components of our method, and that it continues to assign reasonable scores when the inputs are modified in certain ways, including the addition of Gaussian noise and the lowering of the resolution. Code is available at https://github.com/Lou1sM/meaningful_image_complexity.

Multiscale anisotropy analysis of second-harmonic generation collagen imaging of human pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers with a minority (< 10%) of patients surviving five years past diagnosis. This could be improved with the development of new imaging modalities for early differentiation of benign and cancerous fibrosis. This study intends to explore the application of a two-photon microscopy technique known as second harmonic generation to PDAC using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy method to quantify collagen organization in fibrotic pancreatic tissue. Forty slides from PDAC patients were obtained and eight images were captured per each tissue category on each slide. Brownian surface motion and white noise images were generated for calibration and testing of a new variable binning approach to the 2D WTMM Anisotropy method. The variable binning method had greater resistance to wavelet scaling effects and white noise images were found to have the lowest anisotropy factor. Cancer and fibrosis had greater anisotropy factors (Fa) at small wavelet scales than normal and normal adjacent tissue. At a larger scale of 21 μm this relationship changed with normal tissue having a higher Fa than all other tissue groups. White noise is the best representative image for isotropy and the 2D WTMM anisotropy method is sensitive to changes induced in collagen by PDAC.

Learning a preconditioner to accelerate compressed sensing reconstructions in MRI.

PurposeTo learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.MethodsA convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies.ResultsThe learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.ConclusionIt is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks.

The Portilla-Simoncelli Texture Model: towards Understanding the Early Visual Cortex

Texture synthesis is a prolific subarea in computer vision where statistical methods are often successful. The Portilla and Simoncelli (PS) texture algorithm is one of such methods that became very popular and has influenced visual perception studies. For many reasons it can still be considered as a state-of-the art texture synthesis algorithm: (i) it generates textures that are often indistinguishable from the original without scrutiny; (ii) it relies on few parameters compared to recent deep learning methods; (iii) recent algorithms often compare to it. Here, we review the scientific impact of this algorithm and give a detailed explanation. Briefly, the PS algorithm synthesizes a new texture by iteratively imposing to a Gaussian white noise image a set of high-order statistics of wavelet coefficients precomputed on a texture example. After few iterations the initial white noise image is transformed into a texture that is similar to the texture example. We provide a fast C++ implementation, evaluate the effect of the algorithm parameters and illustrate its capabilities with many synthesis examples. In addition, we propose two notable new features to the original implementation: (i) the possibility to interpolate between two textures; (ii) the possibility to handle non-periodicity using the 'periodic+smooth' decomposition.

Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

With the emergence of cloud computing, large amounts of private data are stored and processed in the cloud. On the other hand, data owners (users) may not want to reveal data information to cloud providers to protect their privacy. Therefore, users may upload encrypted data to the cloud or third-party platforms, such as Google Cloud, Amazon Web Service, and Microsoft Azure. Conventionally, data must be decrypted before being analyzed in the cloud, which raises privacy concerns. Moreover, decryption of big data such as images requires enormous computation resources, which is unsuitable for energy-constrained devices, particularly Internet of Things (IoT) devices. Data privacy in most popular applications, such as image query or classification, can be preserved if encrypted images can be directly classified on the cloud or IoT devices without decryption. This paper proposes a high-speed double random phase encoding (DRPE) technique of encrypting images into white-noise images. DRPE-encrypted images are then uploaded and stored in the cloud. Images that are encrypted without being decrypted are classified using deep convolutional neural networks in the cloud. The simulation results indicated the feasibility and good performance of the proposed approach. The proposed privacy-preserving image classification method can be useful in data-sensitive fields, such as medicine and transportation.

BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising

Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods. However, they have been facing two serious problems in the trade-off between noise removal, artifacts, and preserving low-contrast features and high-frequency details. In particular, deep convolutional neural networks might fail to remove strong noise in regions with higher noise levels while completely erasing low-contrast features and high-frequency details. By contrast, compared with conventional deep convolutional neural networks, generative adversarial networks might be better in balancing between erasing different types of noise and recovering texture details. However, they often generate fake details and unexpected artifacts in the image owing to the instability of their discriminator during training. In this study, we explored an innovative strategy for handling the serious problems of image denoising. With this strategy, we propose a novel boosting generative adversarial network (BoostNet) that not only combines all advantages of a generative adversarial sub-network and a deep convolutional neural network, it also successfully avoids the serious problems caused by the corruption and instability of training. BoostNet is developed by integrating a stand-alone deep convolutional neural network and a robust generative adversarial network into an ensemble network, which can effectively boost the denoising performance. We conducted several experiments using challenging datasets of additive white Gaussian noise and real-world noisy images. The experimental results show that our proposed method is superior to other state-of-the-art denoisers in terms of quantitative metrics and visual quality. Our source codes and datasets for BoostNet are available at https://github.com/ZeroZero19/BoostNet.git .

Efficient symmetric image encryption by using a novel 2D chaotic system

The authors know that a common and effective way to protect digital images security are to encrypt these images into white noise image. In this study, the authors have designed a new two-dimensional (2D) chaotic system which is derived from two existing one-dimensional (1D) chaotic maps. The simulation results show that the new 2D chaotic system is able to produce many 2D chaotic maps by selecting different 1D chaotic maps, and which have the wider chaotic ranges and more complex chaotic behaviours compared with the existing 1D chaotic maps. In order to investigate its applications, using the proposed 2D chaotic maps, the authors design a novel image encryption algorithm. First of all, the original image is scrambled by using the chaotic sequences which are generated by new 2D chaotic maps, Arnold transform and Hilbert curve. Then the scrambled image is confused and diffused by chaotic sequences. Finally, the performance of the proposed encryption algorithm is simulated and the experimental results show that the validity and reliability of the proposed algorithm is validated.

Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks.

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.

Implementation of Pixel Likeness Weighted Frame (Plwf) Filter Technique Based Digital Image Denoising for DSP Applications

Digital images are often corrupted by contaminated display and information quality noise. Images can be corrupted at any stage during which they are acquired and transmitted through the media. Image denoising is a basic function designed to eliminate noise from naturally corrupted images. This work proposes a fixed-point discrete wavelet transform (DWT) architecture that uses a nonlinearly modified pixel-like weighted frame (PLWF) technique to denoise the high-throughput of adaptive white Gaussian white noise (AWGN) images. The linearized state to be based on the neighboring pixel unity is that the state model noise is used to improve the peak signal to the sound rate (PSNR). The proposed architecture is employed in two different stages - consistent and conditional sorting output selection unit. The detailed result of the proposed architecture shows the size and display quality of any state-of-the-art performance and some recently introduced work. For further evaluation of the denoising capability, the algorithm is compared to some state-of-the-art algorithms and experimental results on simulated sound images and captured images of low-light noise especially large image processes Low noise light picked up by the test results. The performance of the proposed method is compared to wavelet thresholds, bilateral filters, non-local averaging filters, and bilateral multi-resolution filters. The study found that the draft production plan is smaller than the wavelet threshold, the bilateral filter, and the non-local means of filtering and larger superior/similar to the method, visual quality, PSNR and image index noise bilateral multi-resolution filter quality.

The Offset-Compensated Nonlocal Filtering of Interferometric Phase

The nonlocal approach, proposed originally for additive white Gaussian noise image filtering, has rapidly gained popularity in many applicative fields and for a large variety of tasks. It has proven especially successful for the restoration of Synthetic Aperture Radar (SAR) images: single-look and multi-look amplitude images, multi-temporal stacks, polarimetric data. Recently, powerful nonlocal filters have been proposed also for Interferometric SAR (InSAR) data, with excellent results. Nonetheless, a severe decay of performance has been observed in regions characterized by a uniform phase gradient, which are relatively common in InSAR images, as they correspond to constant slope terrains. This inconvenience is ultimately due to the rare patch effect, the lack of suitable predictors for the target patch. In this paper, to address this problem, we propose the use of offset-compensated similarity measures in nonlocal filtering. With this approach, the set of candidate predictors is augmented by including patches that differ from the target only for a constant phase offset, which is automatically estimated and compensated. We develop offset-compensated versions of both basic nonlocal means and InSAR-Block-Matching 3D (BM3D), a state-of-the-art InSAR phase filter. Experiments on simulated images and real-world TanDEM-X SAR interferometric pairs prove the effectiveness of the proposed method.

Combined Blockiness, Blurriness and White Noise Distortion Meter

Due to discrete cosine transform (DCT) based compression, the loss appears in the form of blocky and blurry distortions in coded pictures. One of the degradation is white noise which corrupts nearly all pixels in the picture. In image transmission system, to assess the image quality at receiver end, lesser image features are sent, instead of the whole reference original picture to accommodate the available bandwidth. The purpose of this research is to propose a procedure which quantifies or estimates the objective picture quality automatically. The presented combined white noise image quality assessment meter (CWNIQAM) functions in the frequency domain for computation of three artefacts blockiness, blurriness and white noise in the corrupted coded images. The designed quality meter at first converts RGB pictures into YCbCr, gray scaled pictures and then edge detection method is applied. Afterward the picture is divided into blocks of 32 × 32 pixels to get local level blockiness, blurriness and white noise value. Next the picture is transformed from spatial to frequency domain and different features called reduced reference parameters are estimated. The composite magnitudes strength through horizontal and vertical harmonics is obtained for estimation of blocky and blurry artefacts in the coded images, while the strength of all ac coefficients and dc component is obtained for computation of white noise. The reduced reference features are computed and compared for reference and coded picture for estimating the particular type of distortion. The uniqueness of the proposed CWNIQAM meter is that, it can estimate the quality of corrupted images by estimating the combination of blockiness, blurriness and white noise through this single meter. The LIVE database2 having 174 different white noise (wn) corrupted images are used for measuring combined objective value of the three artefacts using the designed reduced reference image quality assessment meter algorithm. The objective results are correlated with subjective wn, DMOS LIVE scores. The correlation of about 98% is attained which indicate that much better picture quality index is achieved by the offered reduced reference combined white noise image quality evaluation meter in the frequency domain.

InSAR-BM3D: A Nonlocal Filter for SAR Interferometric Phase Restoration

The block-matching 3-D (BM3D) algorithm, based on the nonlocal approach, is one of the most effective methods to date for additive white Gaussian noise image denoising. Likewise, its extension to synthetic aperture radar (SAR) amplitude images, SAR-BM3D, is a state-of-the-art SAR despeckling algorithm. In this paper, we further extend BM3D to address the restoration of SAR interferometric phase images. While keeping the general structure of BM3D, its processing steps are modified to take into account the peculiarities of the SAR interferometry signal. Experiments on simulated and real-world Tandem-X SAR interferometric pairs prove the effectiveness of the proposed method.

SU-E-I-48: Noise Reduction with Over-Sampling for High Resolution Detectors Using a Spread Function Convolution Method

Purpose: A method to reduce noise resulting from the use of higher resolution x-ray detectors being developed to meet the demands of image guided vascular interventions is demonstrated. Methods: New direct detectors based on amorphous Se can have MTFs that remain high even at their Nyquist frequency. Since such detectors can be made with smaller pixels than may be required for even the high resolution requirements of many neurovascular applications, the resulting over-sampled images can be convolved with various functions to lower the noise. The effect on resolution can then be compared with a simple pixel binning. The general method proposed by Cunningham et al as the apodized-aperture pixel (AAP) design was compared with the Result for various simple 1D convolution spread functions with the effects on total noise and MTF compared to that of the binning case for a 25 μm aSe CMOS detector's MTF published by the University of Waterloo group lead by KS Karim. Results: Assuming a white noise image, various convolution kernels resulted in similar reductions of total standard deviation. Detailed comparisons were made with the simple the 2x binning case. While reducing noise, the over-sampling convolution method using simple convolution low pass filters did not show advantage over 2x binning with regard to modifying the MTF; however, significant improvement was evidenced for the more complex sinc function used in the AAP design. Conclusion: As higher resolution detectors are being developed to meet the increasing demands for improved images to guide finer vascular interventions, use of super-sampled aSe detectors where resolution is maintained with reduced noise may well fill this requirement. Partial support from NIH Grant R01EB002873 and Toshiba Medical Systems Equipment Grant

The Heeger &amp; Bergen Pyramid Based Texture Synthesis Algorithm

This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize a texture by its first-order statistics of both its color and its responses to multiscale and multi-orientation filters, namely the steerable pyramid. The Heeger-Bergen algorithm consists in the following procedure: starting from a white noise image, histogram matchings are performed to the noise alternatively in both the image domain and steerable pyramid domain, so that the corresponding histograms match the ones of the input texture.

Asymmetric encryption method in 4f system based on interference

An asymmetric encryption method in 4f system based on interference is proposed in this paper. The original image is transformed into two white stationary noise images, and the decryption keys are designed to be different from the encryption keys to realize asymmetric encryption. The decryption process is implemented in a 4f system of image addition with grating. This method has very high security as the asymmetric cryptosystem encryption system but also can be easy implemented in optical system. Besides, holographic plate or photorefractive crystal is not necessary in the encryption process. The simulation results prove the feasibility and security of this method.

Self-adaptive image denoising based on bidimensional empirical mode decomposition (BEMD).

To better analyze images with the Gaussian white noise, it is necessary to remove the noise before image processing. In this paper, we propose a self-adaptive image denoising method based on bidimensional empirical mode decomposition (BEMD). Firstly, normal probability plot confirms that 2D-IMF of Gaussian white noise images decomposed by BEMD follow the normal distribution. Secondly, energy estimation equation of the ith 2D-IMF (i=2,3,4,......) is proposed referencing that of ith IMF (i=2,3,4,......) obtained by empirical mode decomposition (EMD). Thirdly, the self-adaptive threshold of each 2D-IMF is calculated. Eventually, the algorithm of the self-adaptive image denoising method based on BEMD is described. From the practical perspective, this is applied for denoising of the magnetic resonance images (MRI) of the brain. And the results show it has a better denoising performance compared with other methods.

Double image encryption based on phase-amplitude hybrid encoding and iterative phase encoding in fractional Fourier transform domains

A new method for double image encryption is proposed that is based on amplitude-phase hybrid encoding and iterative random phase encoding in fractional Fourier transform (FrFT) domains. In the iterative random phase encoding operation, a binary random matrix is defined to encode two original images to a single complex-valued image, which is then converted into a stationary white noise image by the iterative phase encoding with FrFTs. Compared with the previous schemes that uses fully phase encoding, the proposed method reduces the difference between two original images in key space and sensitivity to the FrFT orders. The primitive images can be retrieved exactly by applying correct keys with initial conditions of chaotic system, the pixel scrambling operation and the FrFT orders. Computer simulations demonstrate that the encryption method has impressively high security level and certain robustness against data loss and noise interference.

Subjective evaluation of image quality measures for white noise and Gaussian blur-distorted images

Image quality assessment has diverse applications. A number of image quality measures (IQMs) are proposed, but none is proved to be true representative of human perception of image quality. We have subjectively investigated spectral distance-based and human visual system (HVS)-based IQMs for their effectiveness in representing the human perception for images corrupted with white noise and Gaussian blur. Two sets of 160 images with various degrees of white noise and Gaussian blur are subjectively evaluated by 50 human subjects, resulting in 16 000 human judgments. On the basis of evaluations, image-independent human perception values are calculated. The perception values are plotted against spectral distance-based and HVS-based IQMs. The performance of quality measures is determined by graphical observations and polynomial curve fitting, resulting in best performance by HVS absolute norm and block spectral phase-magnitude error for white noise and Gaussian blur distortions, respectively. It is also observed that the performances of various quality measures differ for different noise distortions, suggesting the use of different quality measures for different noise types rather than a single universal IQM.