Alan C. Bovik

TL;DR: This work proposes a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian response.

TL;DR: The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, and without estimating a depth-dependent transmission map.

TL;DR: This comprehensive and state-of-the art approach to image processing gives engineers and students a thorough introduction, and includes full coverage of key applications: image watermarking, fingerprint recognition, face recognition and iris recognition and medical imaging.

TL;DR: A new approach that can blindly measure blocking artifacts in images without reference to the originals is proposed, which has the flexibility to integrate human visual system features such as the luminance and the texture masking effects.

TL;DR: The LIVE In the Wild Image Quality Challenge Database is designed and created, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices, and a new online crowdsourcing system is implemented, which is used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study.