Rony Ferzli

A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.

A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling

A no-reference perceptual sharpness quality metric, inspired by visual attention information, is presented for a better simulation of the Human Visual System (HVS) response to blur distortions. Saliency information about a scene is used to accentuate blur distortions around edges present in conspicuous areas and attenuate those distortions present in the rest of the image. Simulation results are presented to illustrate the performance of the proposed metric.

No-reference objective wavelet based noise immune image sharpness metric

This paper focuses on no-reference image sharpness/blurriness metrics due to their importance in image, video, and biomedical applications. Simulation results show that existing no-reference objective image sharpness metrics fail to predict correctly the sharpness of images in the presence of noise. A noise-immune wavelet-based sharpness metric is proposed based on the Lipschitz regularity for differentiating between edges and noise singularities. Comparison results reveal the superiority of the proposed method when dealing with a moderate noisy environment.

A No-Reference Objective Image Sharpness Metric Based on Just-Noticeable Blur and Probability Summation

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur (JNB) into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness.

Mobile cloud computing educational tool for image/video processing algorithms

This paper shows the importance and benefit of coupling cloud computing with mobile especially due to power limitations that mobile devices exhibit. Moreover, the work done shows that mobile computing can be applied for educational purposes, where a tool for students termed Mobi4Ed is presented. This educational tool aims at exploiting the concept of cloud computing in the context of image and video processing, where students can assess several algorithms in real-time. Two possible system architectures are detailed, where one uses the cellular channel and the other uses the data channel. Consequently, one of the approaches is adopted and a detailed simulation is done where an Android client device is shown communicating with a server running openCV and using the Haar face-detection algorithm. The work assures the credibility of the adopted system architecture, in terms of deployment, and several future lines of work are proposed.

Human Visual System Based No-Reference Objective Image Sharpness Metric

This work is motivated by the fact that existing no-reference sharpness metrics fail in predicting the correct amount of blurriness in images with different contexts. This paper presents a no-reference objective sharpness metric that can be applied to images with different contexts. The metric combines a human visual system (HVS)-based sharpness perception model as well as a local features extractor resulting in a content-invariant metric. The proposed HVS-based sharpness perception model is derived from performed subjective tests. Simulation results and comparison with existing no-reference sharpness metrics show that the proposed HVS-based sharpness metric correlates well with the perceived sharpness and is able to correctly predict the relative amount of blurriness in images with different contexts.

A cloning approach to classifier training

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.

An enhanced first-order sigma-delta modulator with a controllable signal-to-noise ratio

This paper presents an enhanced first-order sigma-delta modulator. The proposed modulator, derived using the Al-Alaoui operator, outperforms the conventional modulator. A comparison is drawn showing that the conventional sigma-delta modulator is a special case of the enhanced sigma-delta modulator. The paper includes an analytical derivation as well as extensive simulations revealing the superiority of the proposed modulator reaching optimal performance

An Efficient Selective Perceptual-Based Super-Resolution Estimator

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.

A Captcha Based on the Human Visual Systems Masking Characteristics

In this paper, a CAPTCHA is presented based on the masking characteristics of the human visual system (HVS). Knowing that noise can be masked by high activity regions and showing that edges can be masked by noise for a human observer while still being detected by machines, the suggested CAPTCHA is composed of English alphabets that are picked randomly and written with a combination of texture and edges with added noise such as to deceive the machine by randomly changing the visibility of characters for humans. The proposed CAPTCHA is highly legible and robust to brute-force attacks and sophisticated object character recognition (OCR) segmentation algorithms