Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression–encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.

Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing

Objective: Endoscopy is a medical diagnostic procedure used to see inside the human body with the help of a camera-attached system called the endoscope. Endoscopic images and videos suffer from specular reflections (or highlight) and can have an adverse impact on the diagnostic quality of images. These scattered white regions severely affect the visual appearance of images for both endoscopists and the computer-aided diagnosis of diseases. Methods & Results: We introduce a new parameter-free matrix decomposition technique to remove the specular reflections. The proposed method decomposes the original image into a highlight-free pseudo-low-rank component and a highlight component. Along with the highlight removal, the approach also removes the boundary artifacts present around the highlight regions, unlike the previous works based on family of Robust Principal Component Analysis (RPCA). The approach is evaluated on three publicly available endoscopy datasets: Kvasir Polyp, Kvasir Normal-Pylorus and Kvasir Capsule datasets. Our evaluation is benchmarked against 4 different state-of-the-art approaches using three different well-used metrics such as Structural Similarity Index Measure (SSIM), Percentage of highlights remaining and Coefficient of Variation (CoV). Conclusions: The results show significant improvements over the compared methods on all three metrics. The approach is further validated for statistical significance where it emerges better than other state-of-the-art approaches.Clinical and Translational Impact Statement—The mathematical concepts of low rank and rank decomposition in matrix algebra are translated to remove specularities in the endoscopic images The result shows the impact of the proposed method in removing specular reflections from endoscopic images indicating improved diagnosis efficiency for both endoscopists and computer-aided diagnosis systems

https://doi.org/10.1109/jtehm.2023.3283444

Parameter-Free Matrix Decomposition for Specular Reflections Removal in Endoscopic Images

Image super resolution is a signal processing technique to post-process a captured image to retrieve its high-resolution version. Majority of the conventional super resolution methods fail to perform in presence of noise. In this paper, a noise robust reconstruction based single image super resolution (SISR) algorithm is proposed, using alternating direction method of multipliers (ADMM) and plug-and-play modeling. The plug-and-play prior concept is incorporated to the two variable update steps in ADMM. Therefore, a fast SISR model and a denoiser are used in ADMM to implement the proposed robust SISR scheme. The experimental results show that the noise performance of the proposed approach is better than the conventional methods. The impact of parameter selection on the performance of the algorithm is experimentally analyzed and the results are presented.

Robust Single Image Super Resolution Employing ADMM with Plug-and-Play Prior.

The multiple Huffman table (MHT) based encryption technique for multimedia encryption gained much popularity, due to its simplicity, less complexity and good compression performance. At the same time, it is proved that the MHT based encryption system is vulnerable to several types of attacks. In this paper, the security issues of the MHT based encryption system are analyzed. On the basis of analysis, the paper proposes a modified version of MHT based encryption system, with minimal impact on its simplicity and compression performance. It is proposed to perform blockwise shuffling of the transformed image (in DCT domain) based on a chaotic map prior to the MHT stage, so that an attacker cannot retrieve the key because of the changes in the spatial arrangement caused by the blockwise shuffling in the transform domain. Moreover, to ensure copyright protection as well as traitor tracing, it is proposed to incorporate joint fingerprinting and decryption (JFD) technique at the receiver stage. The system is subjected to the known forms of attacks and it has been proved that the proposed system is able to resist the same with minimal level of increase in the system complexity.

A New Framework for Encryption and Authentication of Multimedia Data

Endoscopic images typically suffer from various image degradation such as specular reflection and motion blur due to camera motion. In this paper, we propose a new matrix decomposition method to eliminate the presence of specular reflections (highlights) from endoscopic images. The proposal combines the characteristics of specular reflections and the features of dimensionality reduction through singular value thresholding (SVT) to obtain a highlight free image. Addition of specular reflections with an image alters the singular values of the image matrix. The algorithm iteratively eliminates the changes that occurred to various singular values until all the highlight pixels are eliminated. In order to avoid any loss of significant information, slicing operation is performed on the residue image matrix remained after the initial process. The useful information is extracted from this residue matrix and reinserted with the semi-low-rank component obtained in SVT operation. The experiments and tests reveal that this new matrix decomposition method eliminates the specular reflections from the endoscopic images.

A New Matrix Decomposition Framework forSpecular Reflection Removal from Endoscopic Images

Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy conditions comprises several visual distortions. It eventually leads to flaws when handled with succeeding computer vision algorithms, namely the object identification and tracking. Additionally, eliminating such unpleasant rainy effects is essential prior to the processing of video footage by suitable algorithms. The present work attempts to formulate a new low-rank tensor recovery based deraining algorithm that enables to remove the rain streaks from video footage. The proposed method detects the rain streaks by adopting optical flow estimation along with the brightness features inherent with the rain streaks. A unified framework comprised of tensor singular value decomposition (t-SVD) based weighted nuclear norm minimisation and tensor total variation (TTV) regularisation effectively removes rain streaks and recovers the original rain-free data from the available rainy data. The use of t-SVD enforces the concept of low rankness and also exploits the temporal redundancy among the video frames. Furthermore, TTV regularisation facilitates to promote the temporal continuity for discriminating most of the natural image contents from sparse rain streaks by preserving piece-wise smoothness of video frames. Comprehensive experimental findings based on real and synthetic data with dynamic background show that the rain streaks are more efficaciously eliminated by adopting the proposed method without much loss in the information.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1409

Sudhish N. George

Papers

Parameter-Free Matrix Decomposition for Specular Reflections Removal in Endoscopic Images

Robust Single Image Super Resolution Employing ADMM with Plug-and-Play Prior.

A New Framework for Encryption and Authentication of Multimedia Data

A New Matrix Decomposition Framework forSpecular Reflection Removal from Endoscopic Images

Tensor total variation regularised low-rank approximation framework for video deraining