Ehsan Nadernejad

Bio: Ehsan Nadernejad is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Backlight & Anisotropic diffusion. The author has an hindex of 12, co-authored 35 publications receiving 589 citations. Previous affiliations of Ehsan Nadernejad include University of Copenhagen & University of Mazandaran.

Edge Detection Techniques: Evaluations and Comparison

Abstract: Edge detection is one of the most commonly used operations in image analysis, and there are probably more algorithms in the literature for enhancing and detecting edges than any other single subject. The reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detections is an essential tool. In this paper, we have compared several techniques for edge detection in image processing. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison.

Modeling Power-Constrained Optimal Backlight Dimming for Color Displays

Abstract: In this paper, we present a framework for modeling color liquid crystal displays (LCDs) having local light-emitting diode (LED) backlight with dimming capability. The proposed framework includes critical aspects like leakage, clipping, light diffusion and human perception of luminance and allows adjustable penalization of power consumption. Based on the framework, we have designed a set of optimization-based backlight dimming algorithms providing a perceptual optimal balance of clipping and leakage, if necessary. The novel algorithms are compared with several other schemes known from the literature, using both objective measures and subjective assessment. The results show that the novel algorithms provide better quality at a given energy level or lower energy at a given quality level.

PDEs-Based Method for Image Enhancement

Abstract: Removing noise from data is often the first step in data analysis. De-noising technique should not be only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, we have compared three recently developed techniques for image enhancement and denoising. These methods are based on the use of partial differential equations, including second order, fourth order, and the complex partial differential. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison. In this study, it is shown that the capability of the PDE-based approaches depends highly on the neighboring structure. Our investigations show that in an image where the energy of noise is low, the complex diffusion method offers a better result in image denoising compared to other methods. However, when the energy of the noise increases, performance of the complex diffusion method declines. In general, for the case when the energy of noise in an image is unpredictable, using the heat equation for image denoising is recommended.

A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

Abstract: In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over segmentation could be avoided. Indeed, the bilateral filtering, as a preprocessing step, eliminates the unnecessary details of the image and results in a few numbers of pixons, faster performance and more robustness against unwanted environmental noises. Then, the obtained pixonal image is segmented using the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.

Despeckle Filtering in Medical Ultrasound Imaging

Abstract: In this paper, we implemented Wiener filter, anisotropic diffusion filter, k-distribution based adaptive filter and wavelet filter to de-speckle in medical ultrasound images. The Wiener filter can improve the image qualities well and simulated power spectrum of speckle can be applied on many situations. The Anisotropic diffusion filter can also de-speckle well as long as we choose reasonable parameters, and it doesn’t need extra information of noise pattern. The K-distribution based adaptive filter can improve the image quality, the method is easy to implement and the statistics is easy to estimate and characterize. The wavelet filter is not suitable for removing the speckle in ultrasound images. Keywords: image processing, image restoration, wiener filter, image de-noisng I. I NTRODUCTION The medical Ultrasound B-scan (brightness scan) echo imaging is acquired by summation of the echo signals from ultrasound scatterers in the ultrasound beam range. The scatterers are from structures, tissue interfaces and tissue microstructures etc. in the body, these scatterers are locally correlated. And the coherent summation of signals

Wiener Reconstruction of The Large Scale Structure

Abstract: The formalism of Wiener filtering is developed here for the purpose of reconstructing the large scale structure of the universe from noisy, sparse and incomplete data. The method is based on a linear minimum variance solution, given data and an assumed \prior model which specifies the covariance matrix of the field to be reconstructed. While earlier applications of the Wiener filter have focused on estimation, namely suppressing the noise in the measured quantities, we extend the method here to perform both prediction and dynamical reconstruction. The Wiener filter is used to predict the values of unmeasured quantities, such as the density field in un-sampled regions of space, or to deconvolve blurred data. The method is developed, within the context of linear gravitational instability theory, to perform dynamical reconstruction of one field which is dynamically related to some other observed field. This is the case, for example, in the reconstruction of the real space galaxy distribution from its redshift distribution When the field to be reconstructed is a Gaussian random field, such as the primordial perturbation field predicted by the canonical model of cosmology, the Wiener filter can be pushed to its fullest potential. In such a case the Wiener estimator coincides with the Bayesian estimator designed to maximize the {\it posterior} probability. The Wiener filter can be also derived by assuming a quadratic regularization function, in analogy with the `Maximum Entropy' method. The mean field obtained by the minimal variance solution can be supplemented with constrained realizations of the Gaussian field to

High dynamic range liquid crystal displays with a mini-LED backlight.

Abstract: We analyze the performance of high dynamic range liquid crystal displays (LCDs) using a two-dimensional local dimming mini-LED backlight. The halo effect of such a HDR display system is investigated by both numerical simulation and human visual perception experiment. The halo effect is mainly governed by two factors: intrinsic LCD contrast ratio (CR) and dimming zone number. Based on our results, to suppress the halo effect to indistinguishable level, a LCD with CR≈5000:1 requires about 200 local dimming zones, while for a LCD with CR≈2000:1 the required dimming zone number is over 3000. Our model provides useful guidelines to optimize the mini-LED backlit LCDs for achieving dynamic contrast ratio comparable to organic LED displays.

Prospects and challenges of mini-LED and micro-LED displays

Abstract: J Soc Inf Display. 2019;27:387–401. Abstract We review the emerging mini/micro–light‐emitting diode (LED) displays featuring high dynamic range and good sunlight readability. For mini‐LED backlit liquid crystal displays (LCDs), we quantitatively evaluate how the device contrast ratio, local dimming zone number, and local light profile affect the image quality. For the emissive mini/micro‐LED displays, the challenges of ambient contrast ratio and size‐dependent power efficiency are analyzed. Two figure‐of‐merits are proposed for optimizing the optical and electrical performances of mini/micro‐LED displays.