Lenna Image Research Articles

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

Generation of Yin-Yang-Pattern-Like Images by Iterative Calculation Using DoG

We propose a non-photorealistic rendering method that converts photographic images into Yin-Yang-pattern-like images represented by smooth curved black and white areas. The proposed method applies two Gaussian filters with different parameter values (Difference of Gaussian: DoG) to photographic images, calculates the difference between the two smoothed images, and adds the difference to photographic images.  By repeating the process, Yin-Yang-pattern-like images are generated. In order to verify the effectiveness of the proposed method, we performed an experiment using Lenna image, and visually confirmed the changes Yin-Yang-pattern-like images generated when the values of the various parameters were changed. In addition, we performed an experiment using various photographic images.

Impulse Noise Removal Based on Hybrid Genetic Algorithm

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

Generation of Arbitrarily-Oriented Ribbed-Pattern Images Using Autocorrelation Coefficient

We propose a non-photorealistic rendering (NPR) method for generating arbitrarily-oriented ribbed-pattern (AORP) images from gray-scale photographic images. Ribbed patterns consist of wavy lines in a certain orientation. Ribbed-pattern images are obtained by superimposing ribbed patterns and photographic images. AORP images can arbitrarily change the orientation in which ribbed patterns occur. The proposed method is executed using autocorrelation coefficient. The proposed method can automatically generate ribbed patterns according to the change in the density of photographic images, and can arbitrarily change the orientation in which ribbed patterns occur by changing the value of the parameter. In order to verify the effectiveness of the proposed method, we conduct experiments using Lenna image and other photographic images. In the experiments, we show that the proposed method can generate AORP images.

Wavelet Thresholding Algorithms for Image Denoising

This paper talks about the wavelet thresholding algorithm for image denoising. Any data, either in the form of signals, or images contains more noise than informations. To make sense out of it, it needs denoising. For that, this paper explains algorithm that makes active use of wavelet thresholding to achieve maximum denoising. For statistical analysis matlab software is used as it comes with wavelet thresholding application. This is then used to process standard lenna image to obtain haar wavelet transform for three levels of decomposition of image. On the contrary daubechies wavelet transform is also applied to the same sample image of lenna. Using Haar Wavelet for image compression has a little bifurcation in Retained Energy and Number of Zeros along x axis. On the other hand Daubechies Wavelet compression with global thresholding on decomposition level 4 for standard image of lenna yields different trend lines between Retained Energy and Number of Zeros. Its applications vastly covers all medias such as image, video, signals, etc. to achieve maximum information. With advances in image denoising, space can be utilized more appropriately as user can be able to save space in his personal devices like mobile phones, laptops, etc. With this user can be able to use or access that free space in order to upload more data, or use it for his computational use. Keywords: Image Denoising, Thresholding, Wavelet Transform

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.

Image Reconstruction and Evaluation: Applications on Micro-Surfaces and Lenna Image Representation

This article develops algorithms for the characterization and the visualization of micro-scale features using a small number of sample points, with the goal of mitigating the measurement shortcomings, which are often destructive or time consuming. The popular measurement techniques that are used in imaging of micro-surfaces include the 3D stylus or interferometric profilometry and Scanning Electron Microscopy (SEM), where both could represent the micro-surface characteristics in terms of 3D dimensional topology and greyscale image, respectively. Such images could be highly dense; therefore, traditional image processing techniques might be computationally expensive. We implement the algorithms in several case studies to rapidly examine the microscopic features of micro-surface of Microelectromechanical System (MEMS), and then we validate the results using a popular greyscale image; i.e., “Lenna” image. The contributions of this research include: First, development of local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives using low resolution images. Second, development of a bending energy algorithm from our modified TPS model for filtering out image defects. Finally, development of a computationally efficient technique, referred to as Windowing, which combines TPS and Linear Sequential Estimation (LSE) methods, to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

Reversible data hiding by adaptive group modification on histogram of prediction errors

In this work, the conventional histogram shifting (HS) based reversible data hiding (RDH) methods are first analyzed and discussed. Then, a novel HS based RDH method is put forward by using the proposed Adaptive Group Modification (AGM) on the histogram of prediction errors. Specifically, in the proposed AGM method, multiple bins are vacated based on their magnitudes and frequencies of occurrences by employing an adaptive strategy. The design goals are to maximize hiding elements while minimizing shifting and modification elements to maintain image high quality by giving priority to the histogram bins utilized for hiding. Furthermore, instead of hiding only one bit at a time, the payload is decomposed into segments and each segment is hidden by modifying a triplet of prediction errors to suppress distortion. Experimental results show that the proposed AGM technique outperforms the current state-of-the-art HS based RDH methods. As a representative result, the proposed method achieves an improvement of 4.30dB in terms of PSNR when 105,000 bits are hidden into the test Lenna image.

Inverse-Orthogonal Jacket-Haar, DCT 변환

본 논문에서는 Hadamard 변환이 Jacket 변환으로 일반화 될 수 있는 것처럼 Haar 변환을 Jacket-Haar 변환으로 일반화 한다. Jacket-Haar 변환의 원소는 0 과 ±2<SUP>k</SUP> 이다. original Haar 변환과 비교해서 Jacket-Haar 변환의 베이시스(basis)는 신호처리에 보다 적합하다. 응용으로 2×2 Hadamard 행렬을 기반으로 한 DCT-II(discrete cosine transform-II)와 2×2 Haar 행렬을 기반으로 한 HWT(Haar Wavelete transform)를 제시하고 이들의 성능을 분석하며 Lenna 이미지의 시뮬레이션을 통해 성능을 평가하였다.

Performance of least mean square equalizer in shallow water acoustic communication channel

In shallow water, a transmitted signal is severely influenced by sea surface and bottom boundaries. Every signal to receiver except the signals through the water medium experiences a time-variant scattering in the sea surface and grazing-angle-dependent bottom reflection loss in the bottom. Consequently, the performance of underwater acoustic communication systems is degraded, and high-speed digital communication is disrupted by inter-symbol interference (ISI) effect. In this study, least mean square (LMS) algorithm equalizer is adopted to cancel out ISI effect. The equalizer is implemented in underwater acoustic communication system. Experiment is conducted in 26 m depth littoral ocean and the standard Lenna image which consists of 50x50 pixels and 8 bits per pixel is transmitted. It is verified that equalization is an efficient way to achieve high transmission rate in shallow water acoustic communication channel. This work was supported by Research Programs of Hanwha Corporation 2011.

Underwater image transmission performance in multi‐path flat fading channel.

Time‐variant sea surface scattering, Doppler spreading due to source/receiver motion, and grazing angle dependent bottom reflection in the multi‐path underwater acoustic channel cause the signal fading in amplitude and phase fluctuation. Consequently, the performance of underwater acoustic communication systems is degraded, and high‐speed digital communication is disrupted. In this study, two different multi‐path channels such as a water tank and a river are considered and the experiments are conducted under flat fading condition. Rayleigh and Rice fading channel models are adopted and their distributions are compared with the measured in experiments. Finally, the standard Lenna image using binary frequency shift keying modulation is transmitted and the quality of the received image is analyzed based on the channel models. It is shown that the quality of the received image and the channel model is consistent with each other.

Unequal loss protection: graceful degradation of image quality over packet erasure channels through forward error correction

We present the unequal loss protection (ULP) framework in which unequal amounts of forward error correction are applied to progressive data to provide graceful degradation of image quality as packet losses increase. We develop a simple algorithm that can find a good assignment within the ULP framework. We use the set partitioning in hierarchical trees coder in this work, but our algorithm can protect any progressive compression scheme. In addition, we promote the use of a PMF of expected channel conditions so that our system can work with almost any model or estimate of packet losses. We find that when optimizing for an exponential packet loss model with a mean loss rate of 20% and using a total rate of 0.2 bits per pixel on the Lenna image, good image quality can be obtained even when 40% of transmitted packets are lost.

Optimal use of Markov models for DPCM picture transmission over noisy channels

Joint source/channel decoders that use the residual redundancy in the source are investigated for differential pulse code modulation (DPCM) picture transmission over a binary symmetric channel. Markov sequence decoders employing the Viterbi algorithm that use first-order source statistics are reviewed, and generalized for decoders that use second-order source statistics. To make optimal use of the source correlation in both horizontal and vertical directions, it is necessary to generalize the conventional Viterbi decoding algorithm for a one higher-dimensional trellis. The paths through the trellis become two-dimensional "sheets", thus, the technique is coined "sheet decoding". By objective [reconstruction signal-to-noise ratio (SNR)] and subjective measure, it is found that the sheet decoders outperform the Markov sequence decoders that use a first-order Markov model, and outperform two other known decoders (modified maximum a posteriori probability and maximal SNR) that use a second-order Markov model. Moreover, it is found that the use of a simple rate-2/3 block code in conjunction with Markov model-aided decoding (MMAD) offers significant performance improvement for a 2-bit DPCM system. For the example Lenna image, it is observed that the rate-2/3 block code is superior to a rate-2/3 convolutional code for channel-error rates higher than 0.035. The block code is easily incorporated into any of the MMAD DPCM systems and results in a 2-bit MMAD DPCM system that significantly outperforms the noncoded 3-bit MMAD DPCM systems for channel-error rates higher than 0.04.

Corrections to "a new error function at hidden layers for fast training of multilayer perceptrons"

(e) (f) Fig. 6. Results for Lenna image: (a) the original Lenna image, (b) 3-D plot of the reconstructed surface by the proposed neural-based method, (c) 3-D plot of the reconstructed surface by the Lambertain model with the estimated light source direction by Zheng and Chellappa’s method, (d) 3-D plot of the reconstructed surface by the Lambertian model with incorrect light source direction, (e) 3-D plot of the reconstructed surface by the Torrance‐Sparrow model with the estimated light source direction by Zheng and Chellappa’s method, and (f) 3-D plot of the reconstructed surface by the Torrance‐Sparrow model with incorrect parameters setting.