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As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain–computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time–frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique—namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

This paper introduces an experimental study on the recognition of the person's face by utilizing three Techniques of extraction: Principle Components Analysis (PCA), Linear Discriminant Analysis (LDA) and Contourlet- Curvelet Transform (CCT). The results of these approaches were observed and compared to discover the perfect scheme for identification of human faces. The tests have been carried out on the faces databases of (ORL),(UMIST), and (JAFFE). The results acquired by the methods were quantified by altering the ratio of train to test photos in three categories: 75/25, 55/45 and 35/65. The evaluation results showed that the CCT extraction method provides better results than the others. The highest recognition rate was recorded for the CCT approach (recognition rate=98.980%) when the (train/test) photos ratio is (75/25). Furthermore, the best recognition rates for the LDA and PCA were 96.391% and 95.127% respectively. The Matlab R2019b program was used for implementing and testing the algorithms.

The new techniques of three-dimensional (3D)-optical coherence tomography (OCT) imaging is very useful for detecting retinal pathologic changes in various diseases and determining retinal thickness ‘abnormalities’. Fundus colour images have been used for several years for detecting retinal abnormalities too. If the two image modalities were combined, the resulted image would be more informative. The first step to combine these two modalities is to register colour fundus images with an en face representation of OCT. In this study, curvelet transform is used to extract vessels for both modalities. Then the extracted vessels from two modalities are registered together in two stages. At first, images are registered using scaling and translation transformations. Then a quadratic transformation model is assumed between two pairs of images; because retina is imaged as a second-order surface. Twenty-two eyes (17 macular and 5 prepapillary), from random patients, were imaged in this study with Topcon 3D OCT1000 instrument. A new registration error is defined which averages the distance between all the corresponding points in two sets of vessels. Results show that registration error after stage one is 6.01 ± 1.82 pixels and after stage two is 1.02 ± 0.02 pixels.

Currently, medical imaging modalities produce different types of medical images to help doctors to diagnose illnesses or injuries. Each modality of images has its specific intensity. Many researchers in medical imaging have attempted to combine redundancy and related information from multiple types of medical images to produce fused medical images that can provide additional concentration and image diagnosis inspired by the information for the medical examination. We propose a new method and method of fusion for multimodal medical images based on the curvelet transform and the genetic algorithm (GA). The application of GA in our method can solve the suspicions and diffuse existing in the input image and can further optimize the characteristics of image fusion. The proposed method has been tested in many sets of medical images and is also compared to recent medical image fusion techniques. The results of our quantitative evaluation and visual analysis indicate that our proposed method produces the best advantage of medical fusion images over other methods, by maintaining perfect data information and color compliance at the base image.

With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..

This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform in two and three dimensions. The first digital transformation is based on unequally spaced fast Fourier transforms, while the second is based on the wrapping of specially selected Fourier samples. The two implementations essentially differ by the choice of spatial grid used to translate curvelets at each scale and angle. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. And both implementations are fast in the sense that they run in O(n^2 n) flops for n by n Cartesian arrays; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity. Our digital transformations improve upon earlier implementations--based upon the first generation of curvelets--in the sense that they are conceptually simpler, faster, and far less redundant. The software CurveLab, which implements both transforms presented in this paper, is available at http://www.curvelet.org.

The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.