Silvia Corchs

Underwater image processing: state of the art of restoration and image enhancement methods

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance.The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have b...

A neurodynamical model for selective visual attention using oscillators

We present a neurodynamical model to study and simulate visual search tasks experiments. The model consists of different pools of interconnected phase oscillators. Each oscillator is described by an integrate-and-fire type equation. Visual attention appears as an emergent property of the dynamic of the system, resulting from the temporal synchronization of the pools which bind the features of the searched target. The time courses observed in the psychophysical visual search experiments can be explained within a purely parallel dynamic and without assuming priority maps and serial spotlight mechanisms, as is usually done in the standard theories. The model fits also the measured activity reported for the neural responses in inferotemporal visual cortex of monkeys performing visual search tasks.

Contrast image correction method

A method for contrast enhancement is proposed. The algorithm is based on a local and image-dependent exponential cor- rection. The technique aims to correct images that simultaneously present overexposed and underexposed regions. To prevent halo artifacts, the bilateral filter is used as the mask of the exponential correction. Depending on the characteristics of the image (piloted by histogram analysis), an automated parameter-tuning step is intro- duced, followed by stretching, clipping, and saturation preserving treatments. Comparisons with other contrast enhancement tech- niques are presented. The Mean Opinion Score (MOS) experiment on grayscale images gives the greatest preference score for our algorithm.

Recall or precision oriented strategies for binary classification of skin pixels

Skin detection is a preliminary step in many applications. We analyze some of the most frequently cited binary skin classifiers based on explicit color cluster definition and present possible strategies to improve their performance. In particular, we demonstrate how this can be accomplished by using genetic algorithms to redefine the cluster boundaries. We also show that the fitness function can be tuned to favor either recall or precision in pixel classification. Some combining strategies are then proposed to further improve the performance of these binary classifiers in terms of recall or precision. Finally, we show that, whatever the method or the strategy employed, the performance can be enhanced by preprocessing the images with a white balance algorithm. All the experiments reported here have been run on a large and heterogeneous image database.

No reference image quality classification for JPEG-distorted images

Abstract In this paper, we address the Image Quality Assessment (IQA) of JPEG-distorted images. We approach the IQA field by focusing on a classification problem that maps different objective metrics into different categorical quality classes. To this end, we adopt a machine learning classification approach, where No Reference (NR) metrics are considered as features, while the assigned classes come from psycho-visual experiments. Eleven NR metrics have been considered: seven specific for blockiness and four general purpose. We evaluate the performance of single metrics and investigate if a pool of metrics can reach better performances than each of the single ones. Five as well as three quality classes are considered, and the corresponding classifiers are tested on two well known databases available in the literature (LIVE and MICT), and on a new database (IVL) presented in this paper.

Video summarization using a neurodynamical model of visual attention

We propose a new approach to select the representative frames for video summarization. The representative frames are selected based on the results of the analysis of the events depicted in the shot in terms of regions of interest (ROIs). These ROIs are obtained from a biologically based computational model of visual attention. To select the video frames part of the final visual summary, we exploit an adaptive temporal sampling method that analyzes the visual feature distribution of the ROIs. Preliminary results are presented and discussed.

Low Quality Image Enhancement Using Visual Attention

Low quality images are often corrupted by artifacts and generally need to be heavily processed to become visually pleasing. We present a modified version of unsharp masking that is able to perform image smoothing, while not only preserving but also enhancing the salient details in images. The premise supporting the work is that biological vision and image reproduction share common principles. The key idea is to process the image locally according to topographic maps obtained from a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing the edges differently according to human perception.

Noisy images-JPEG compressed: subjective and objective image quality evaluation

The aim of this work is to study image quality of both single and multiply distorted images. We address the case of images corrupted by Gaussian noise or JPEG compressed as single distortion cases and images corrupted by Gaussian noise and then JPEG compressed, as multiply distortion case. Subjective studies were conducted in two parts to obtain human judgments on the single and multiply distorted images. We study how these subjective data correlate with No Reference state-of-the-art quality metrics. We also investigate proper combining of No Reference metrics to achieve better performance. Results are analyzed and compared in terms of correlation coefficients.

Predicting Complexity Perception of Real World Images

The aim of this work is to predict the complexity perception of real world images. We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images.

Genetic programming approach to evaluate complexity of texture images

Abstract. We adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure MGP exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. MGP outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance.