From announce at list.ipol.im Fri Dec 7 00:15:51 2018 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Fri, 7 Dec 2018 00:15:51 +0100 Subject: [IPOL announce] new article: An Analysis and Implementation of the Shape Preserving Local Histogram Modification Algorithm Message-ID: A new article is available in IPOL: http://www.ipol.im/pub/art/2018/236/ Jose-Luis Lisani, An Analysis and Implementation of the Shape Preserving Local Histogram Modification Algorithm, Image Processing On Line, 8 (2018), pp. 408?434. https://doi.org/10.5201/ipol.2018.236 Abstract In this paper we describe the implementation of the algorithm for local contrast enhancement published by Caselles et al. in 1999. This algorithm was the first designed explicitly to increase the contrast while preserving the so-called 'shape structure' of the image, that is, its set of level sets. According to the mathematical morphology school, artifacts are created when this structure is modified. The original algorithm is described and also two alternative implementations are proposed, which limit the over-enhancement of noise. From announce at list.ipol.im Wed Dec 19 00:07:13 2018 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Wed, 19 Dec 2018 00:07:13 +0100 Subject: [IPOL announce] new article: Improvements of the Inverse Compositional Algorithm for Parametric Motion Estimation Message-ID: A new article is available in IPOL: http://www.ipol.im/pub/art/2018/222/ Thibaud Briand, Gabriele Facciolo, and Javier S?nchez, Improvements of the Inverse Compositional Algorithm for Parametric Motion Estimation, Image Processing On Line, 8 (2018), pp. 435?464. https://doi.org/10.5201/ipol.2018.222 Abstract In this work, we propose several improvements of the inverse compositional algorithm for parametric registration. We propose an improved handling of boundary pixels, a different color handling and gradient estimation, and the possibility to skip scales in the multiscale coarse-to-fine scheme. In an experimental part, we analyze the influence of the modifications. The estimation accuracy is at least improved by a factor 1.3 while the computation time is at least reduced by a factor 2.2 for color images. From announce at list.ipol.im Sun Dec 23 20:23:36 2018 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Sun, 23 Dec 2018 20:23:36 +0100 Subject: [IPOL announce] new article: EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned on a Large Set of Patches Message-ID: A new article is available in IPOL: https://www.ipol.im/pub/art/2018/242/ Samuel Hurault, Thibaud Ehret, and Pablo Arias, EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned on a Large Set of Patches, Image Processing On Line, 8 (2018), pp. 465?489. https://doi.org/10.5201/ipol.2018.242 Abstract The Expected Patch Log-Likelihood method, introduced by Zoran and Weiss, allows for whole image restoration using a patch-based prior (in the likelihood sense) for which a maximum a-posteriori (MAP) estimate can be calculated. The prior used is a Gaussian mixture model whose parameters are learned from a dataset of natural images. This article presents a detailed implementation of the algorithm in the context of denoising of images contaminated with white additive Gaussian noise. In addition, two possible extensions of the algorithm to handle color images are compared. From announce at list.ipol.im Mon Dec 24 19:41:16 2018 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Mon, 24 Dec 2018 19:41:16 +0100 Subject: [IPOL announce] new article: An Affine Invariant Patch Similarity Message-ID: A new article is available in IPOL: http://www.ipol.im/pub/art/2018/202/ Vadim Fedorov, and Coloma Ballester, An Affine Invariant Patch Similarity, Image Processing On Line, 8 (2018), pp. 490?513. https://doi.org/10.5201/ipol.2018.202 Abstract Image and video comparison is often approached by comparing patches of visual information. In this work we present a detailed description and implementation of an affine invariant patch similarity measure that performs an appropriate patch comparison by automatically and intrinsically adapting the size and shape of the patches. We also describe the complete implementation of the proposed iterative algorithm for computation of those shape-adaptive patches around each point in the image domain.