Alan C. Bovik

Bio: Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.

Relating analog and digital order statistic filters

Abstract: Recent efforts have been directed towards the development of analog versions of the digital median filter. Here, the concept is extended to the more general rank-order and order statistic (OS) filters. By identifying analog and digital signals that are in some sense equivalent, such that the outputs of analog and digital OS filters applied to equivalent analog and digital signals are also equivalent, the properties of discrete-time and continuous-time OS filters can be related, making possible a richer mathematical framework for analysis and design of rank-order and OS filters in both domains. While some theory for digital rank-order and OS filters has been developed, the definition of the analog versions is nontrivial. Suitable definitions are offered for analog versions of the rank-order and OS filters, and a formalism is developed for relating the analog and digital filters. >

Nonlinearities in Stereoscopic Phase-Differencing

Abstract: Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, these phase-differencing techniques suffer a significant impediment: phase nonlinearities. In regions of phase nonlinearity, the signals under consideration possess properties that invalidate the use of phase for disparity estimation. This paper uses the amenable properties of Gaussian white noise images to analytically quantify these properties. The improved understanding gained from this analysis enables us to better understand current methodologies for detecting regions of phase instability. Most importantly, we introduce a new, more effective means for identifying these regions based on the second derivative of phase.

Sampled efficient full-reference image quality assessment models

Abstract: Existing Ml-reference image quality assessment models first compute a full image quality-predictive feature map followed by a spatial pooling scheme, thereby producing a single quality score. Here we study spatial sampling strategies that can be used to more efficiently compute reliable picture quality scores. We develop a random sampling scheme on single scale full-reference image quality assessment models. Based on a thorough analysis of how this random sampling strategy affects the correlations of the resulting pooled scores against human subjective quality judgements, a highly efficient grid sampling scheme is proposed which replaces the ubiquitous convolution operations with local block-based multiplications. Experiments on four different databases show that this block-based sampling strategy can yield results similar to methods that use a complete image feature map, even when the number of feature samples is reduced by 90%.

Relating spatial and spectral models of oriented bandpass natural images

Abstract: Univariate models of the Natural Scene Statistics (NSS) of perceived digital pictures have been deployed in a wide variety of image and video processing applications. However, much less effort has been made towards understanding, modeling, and using bivariate image NSS. Towards filling this gap, Su et al. developed a closed form correlation model of oriented bandpass natural images applicable to adjacent pixels. We later extended this model to account for pixels separated by larger spatial distance. Here, we expand our previous work further to model the bivariate responses of bandpass spatial filters covering a wider range of bandwidths. Furthermore, we study the relationship between the parameters of the closed form correlation model and image spectral models.

Stereoscopic versus monoscopic detection of masses on breast tomosynthesis projection images

Abstract: The goal of this study was to assess if stereoscopic viewing of breast tomosynthesis projection images impacted mass detection performance when compared to monoscopic viewing. The dataset for this study, provided by Hologic, Inc., contained 47 craniocaudal cases (23 biopsy proven malignant masses and 24 normals). Two projection images that were separated by 8 degrees were chosen to form a stereoscopic pair. The images were preprocessed to enhance their contrast and were presented on a stereoscopic display. Three experienced breast imagers participated in a blinded observer study as readers. Each case was shown twice to each reader - once in the stereoscopic mode, and once in the monoscopic mode in a random order. The readers were asked to make a binary decision on whether they saw a mass for which they would initiate a diagnostic workup or not, and also report the location of the mass and provide a confidence score in the range of 0-100. The binary decisions were analyzed using the sensitivity-specificity measure, while the confidence scores were analyzed using the Receiver Operating Characteristic curve (ROC). We also report a statistical analysis of the difference in partial AUC values greater than 95% sensitivity between the stereoscopic and monoscopic modes.

Image quality assessment: from error visibility to structural similarity

Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

A wavelet tour of signal processing

Abstract: Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These