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.

Numerical Analysis of Image Patterns

Abstract: We find similarities between spatial pattern analysis and other low-level cooperative visual processes. Numerical algorithms for computing intrinsic scene attributes, e.g. shape-from-X (shading, texture, etc.) and optical flow typically involve estimating generalized orientation components via iterative constraint propagation. Smoothing or regularizing terms imposed on the constraint equations often enhance the uniqueness / stability (well-posedness) of the solutions. The numerical approach to visual pattern analysis developed here proceeds analogously via estimation of emergent 2-D image frequencies. Unlike shape-from-X or optical flow paradigms, constraints are derived from the responses of multiple oriented spatial frequency channels rather than directly from the image irradiance measurements. By using channel filters that are sufficiently concentrated in both space and frequency, highly accurate spatial frequency estimates are computed on a local basis. Two methods are proposed. In the first, constrained estimates of the emergent image frequencies are obtained by resolving the responses of multiple channel filters in a process similar to photometric stereo. The second approach formulates the estimation of frequencies as an extremum problem regularized by a smoothing term. An iterative constraint propagation algorithm is developed analogous to those used in variational / relaxational approaches to shape-from-X (shading, texture) and optical flow. Examples illustrate each approach using synthetic and natural images.

Channel interactions in visible pattern analysis

Abstract: The efficacy of the 2-D Gabor channel filters for segmenting images based on the division of the image into textures was established previously. In the present work, the authors extend the model for the direct estimation of the emergent spatially localized orientation/frequencies of visible patterns using a variational scheme. The most significant result of this extension is that patterns with space-varying frequency characteristics, arising from, e.g. surface deformation, can still be analyzed and segregated. Moreover, the information extracted can provide a means for developing shape-from-texture algorithms that are optimally localized. The implementation of the filtering scheme is described. >

Non-uniformity correction of IR images using natural scene statistics

Abstract: Infrared images are commonly afflicted by distortions such as non-uniformity. Non-uniformity is characterized by horizontal and vertical fixed pattern noise. Accurately estimating the amount of non-uniformity present in an image and removing that amount of non-uniformity noise are open problems. Several estimators of non-uniformity exist, but their ability to estimate degrades with the presence of other sources of noise. Specifically, most of these metrics lack the robustness demanded by a more complete non-uniformity model. Previous non-uniformity correction algorithms are compared and found to underperform relative to a more complete model of non-uniformity that we have developed. Using this model, we have created a new denoising algorithm, which we call the Gaussian scale mixture perceptual pattern denoiser. The new model and algorithm can fully characterize non-uniformity using covariance matrices.

Range segmentation using focus cues

Abstract: A new scene segmentation scheme is proposed. A scene with different depth ranges (e.g., with multiple objects) is segmented into ranges. Focus cues are used to measure depth ranges and segmentation is performed using a multiresolution approach. To use focus cues in a multiresolution framework, we develop a criterion function and pyramid for focus measure. The segmentation algorithm first segments a scene at a coarse resolution and proceeds progressively to finer resolutions. This segmentation into ranges gives a description of the 3-dimensional structure of a scene. This segmentation method does not require any prior knowledge of depth ranges. The segmentation results for a scene with multiple objects with different depth ranges is presented.

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