Zoran A. Ivanovski

Robust super-resolution based on pixel-level selectivity

In this paper, a new technique for robust super-resolution (SR) from compressed video is presented. The proposed method exploits the differences between low-resolution images at the pixel level, in order to determine the usability of every pixel in the low-resolution images for SR enhancement. Only the pixels, from the lowresolution images, that are determined to be usable, are included in the L2-norm minimization procedure. Three different usability criterions are proposed, maximum distance from the median - MDM, maximum distance from initial image - MDIM, and maximum distance from the SR estimate - MDSRE. The results obtained with real video sequences demonstrate superior quality of the resulting enhanced image in the presence of outliers and same quality without outliers when compared to existing L2-norm minimization techniques. At the same time, the proposed scheme produces sharper images as compared to L1-norm minimization techniques.

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.

An adaptive POCS algorithm for compression artifacts removal

An effective compression artifacts removal algorithm is proposed based on the theory of projections onto convex sets (POCS). It includes a block classification procedure, a ringing detection procedure, prediction of the spatial distribution of the quantization errors and estimation of the visibility of the compression artifacts. Information gained from both the spatial and transform domains, is incorporated into adaptive projections. Experiments performed on JPEG-compressed images, demonstrate the effectiveness of the proposed algorithm in suppressing both blocking and ringing artifacts, as well as the ability of the algorithm to preserve the image sharpness.

Fully automated exposure fusion algorithm for mobile platforms

In this paper, an automated algorithm for fusion of differently exposed images is proposed. The algorithm is based on Gaussian/Laplacian pyramid decomposition of the input images. It includes optimization of the pyramid height for best quality results, and decision module for the necessity of the procedure for the recorded scene. The end-user involvement in the process of the creation of the output image is completely eliminated, making this algorithm a good choice for use on a mobile platform, as add-on software for low price mobile cameras. Experimental results show high efficiency of the algorithm and excellent visual quality of the resulting images.

A real time algorithm for exposure fusion of digital images

A real time algorithm for fusion of differently exposed images is proposed in this paper. The algorithm blends the details from two images of high dynamic range scene, acquired with different exposure values, into one output image which can be displayed on low dynamic range devices. The blending is performed in the spatial domain, using pixel by pixel approach, thus eliminating the need for expensive block processing or transform domain coding. The proposed scheme works both on grey and color images. The algorithm shows high efficiency, which make it applicable on low processing power platforms, such as mobile devices. The obtained results are visually comparable with previously published algorithms that are computationally much more expensive.

Customized k-space trajectory for compressed sensing MRI

The paper presents a method for spectrum customized k-space sampling for Magnetic resonance imaging. The proposed k-space trajectory is customized for certain types of Magnetic resonance images. Experimental testing has shown that this sampling method is suitable for compressed sensing MR Imaging because the customized mask captures most of the k-space energy in only a small number of samples.

An efficient, SELective, Perceptual-based super-resolution estimator

In this paper, a selective perceptual-based (SELP) scheme 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 (HVS) model is proposed to compute the contrast sensitivity threshold for a given background intensity. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. This is accomplished by estimating the contrast sensitivity threshold locally over a block. Next, the absolute difference between each pixel and its neighbors is computed and compared to the threshold upon which a decision is made to include the pixel in the SR estimator for the next iteration or not. The perceptual model is integrated into a MAP-based SR algorithm as well as a fast ML estimator. Simulation results show up to 47% reduction on average in computational complexity with comparable SNR gains and visual quality.

Kernel power flow orientation coefficients for noise-robust speech recognition

Noise-robustness has become a crucial parameter in Automatic Speech Recognition (ASR) systems today with their increased use in noise-filled real-world environments. One way to address this issue is to develop features that are innately noise-robust. The Kernel Power flow Orientation Coefficients (KPOCs) are a novel feature set based on spectro-temporal analysis that uses a bank of 2D kernels to extract the dominant orientation of the power flow at each point in the auditory spectrogram of the speech signal. The collection of dominant power flow orientation angles forms a novel representation of the speech signal named the Power flow Orientation Spectrogram (POS), which is innately resistant to the spectral masking introduced by the presence of noise and reverberation. This approach not only grants KPOC its noise robustness, but also keeps the number of output coefficients inherently small, thus eliminating the need of the feature dimensionality reduction otherwise necessary in the conventional the spectro-temporal approach. KPOCs performance has been evaluated on three experimental frameworks, and the results have shown that they outperform a number of well-known noise-robust features for average and low SNRs. The relative improvement in Word Recognition Accuracy (WRA) to the classic Mel Frequency Cepstral Coefficients (MFCCs) for the Aurora 2 task goes from 32% up to 190% for SNRs in the range from 10 down to - 5 dB. The experimental results also show that in clean training the performance of KPOC approaches that of the state-of-the-art noise-robust ASR frontends in all noise scenarios for small vocabulary ASR tasks.

Automatic image orientation detection with prior hierarchical content-based classification

This paper presents an algorithm for automatic detection of the orientation of user generated images. The images can initially be into 3 different orientations. The algorithm utilizes SVM classifier trained over feature vectors of the low-level characteristics of the images in the training set. In order to increase classification accuracy, prior to the SVM classification, the images are hierarchically pre-classified into different groups regarding to the semantic cues they contain, like presence and absence of sky, light, or human faces. Then separate SVM classifier is trained for each group. Also, the paper presents the conclusions of an online survey about the user preferences for software for automatic image orientation detection and gives explanation how those conclusions correspond to the accuracy of the proposed algorithm.

Analysis of extracted pitch contours across speakers for intonation modelling in TTS synthesis

The paper presents the results of an analysis of extracted pitch contours of spoken Macedonian across 7 native speakers and 3 discourse contexts. The 125 analyzed intonation phrases (IPs) were taken from a speech corpus recorded for this purpose. Pitch contours were extracted for each of the phrases, and then normalized to the speakers mode. This allowed group analysis of the contours. In total 8 groups were created based on speaker sex and 4 different discourse functions. Each group was then statistically analyzed and average normalized pitch as well as upper and lower bound vectors were calculated. The algorithms designed for this purpose have been described in detail. The calculated vectors were used as the basis for building linearsegment models used for automatic intonation generation in a text-to-speech (TTS) synthesis system.