Signal Processing (Elsevier): Image Restoration and Enhancement: Recent Advances and Applications Call for Papers

Call for Papers Signal Processing Special Issue on Image Restoration and Enhancement: Recent Advances and Applications Image restoration and enhancement is a classical research area in image processing. Previously, adaptive local and nonlocal approximations have been popular. Local approximations attempt to estimate the image content in a locally adaptive neighborhood. Nonlocal methods exploit the self-similarity within the whole image without the constraint of locality. The former tends to be more efficient and the latter would produce better results. Recently, learning-based techniques adopting advances in machine learning and computer vision, such as sparse coding and dictionary learning, have attracted much more attention and been applied to image/video restoration and enhancement. These techniques can represent image contents better using learned dictionaries. In addition, some novel application areas, e.g., legacy photos and paintings, HD/3D displays, mobile and portable devices, and web-scale data, have prompted new research interests in image/video restoration and enhancement. This special issue aims to promote research in image restoration and enhancement in a modern era by revisiting classical methods, proposing new techniques, and boosting novel applications. Topics of interest include, but are not limited to: Local versus nonlocal approximations Sparse and redundant representations Adaptive transforms Dictionary learning Nonparametric approximations Biologically inspired models Multi-resolution and hierarchical processing Single-image super-resolution Applications: denoising and artifacts removal Applications: sharpness, contrast, and resolution enhancement Applications in medical images (CT, MRI, ultrasound, etc.) New applications: legacy materials, HD/3D/mobile displays, web-scale data, etc. Prospective authors should visit http://www.elsevier.com/journals/signal-processing/0165-1684/guide-for-authors for information on paper submission. Manuscripts should be submitted using the online submission system at http://ees.elsevier.com/sigpro/. Please choose "SI: Image Restoration" as the manuscript type. Important Dates: Manuscript submission due: 1 June, 2013 First review completed: 1 August, 2013 Revised manuscript due: 1 September, 2013 Second review completed: 1 October, 2013 Final manuscript due: 1 November, 2013 Guest editors: Ling Shao, The University of Sheffield, UK Xinbo Gao, Xidian University, China Houqiang Li, University of Science and Technology of China