A Fuzzy Homomorphic Algorithm for Image Enhancement

The implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The technique combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of image enhancement. This algorithm reduces the computational complexity by eliminating the need for image-size-dependent filter kernels and the forward and inverse Fourier Transforms.   The proposed algorithm is compared with the more established algorithms for the enhancement of low contrast images with uneven illumination. The results show that the fuzzy method provides similar or better results than the frequency domain method and some other well-known image enhancement algorithms

Multi-class 3D region growing algorithm

This paper describes generalization of multi-class region growing algorithm allowing for segmentation of 3D images (series of slices). The multi-class region growing algorithm was proposed in [1]. Additionally, a new method for finding the start region was presented. As its 2D version the new algorithm does not need initial parameters since it features segmentation quality assessment. A series of segmentations is performed on a dataset, each segmentation quality is assessed and the best one is picked. Additionally, the number of classes in the image is determined automatically. The multi-class 3D region growing algorithm is tested on CT and MRI scans. Different types of MRI scans are used. The scans come from multiple sources. The results are shown in the form of 3D reconstruction accompanied by a selected 2D slice. In addition, such selected algorithm performance issues are discussed as effective algorithm implementation; fast dilation implementation is also mentioned. The paper explains all concepts and operations used by the algorithm. It includes numerous figures and algorithm pseudo-code descriptions

General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

12 pagesInternational audienceThis paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP) approach [1–3], which has been recently introduced. An intensity image is represented with a set of local neighborhoods defined for each point of the image to be studied. These so-called General Adaptive Neighborhoods (GANs) are simultaneously adaptive with the spatial structures, the analyzing scales and the physical settings of the image to be addressed and/or the human visual system. After a brief theoretical introductory survey, the GANIP approach will be successfully applied on real application examples in image restoration, enhancement and segmentation

Les applications industrielles et biomédicales du modèle LIP (Logarithmic Image Processing)

Le modèle LIP (Logarithmic Image Processing) est un cadre mathématique rigoureux non linéaire compatible physiquement avec les images à signal transmis et la vision humaine. Le cadre mathématique du modèle LIP fournit un ensemble spécifique d'opérations algébriques et fonctionnelles, permettant ainsi l'introduction de techniques nouvelles de traitement d'images, physiquement et mathématiquement justifiées, qui donnent de bons résultats dans des cadres applicatifs concrets. Cet article présente les applications biomédicales et industrielles utilisant les techniques d'interpolation, de recentrage et rehaussement de dynamique, de calcul de contraste et de corrélation, basées sur les opérations non-linéaires du modèle LIP. Les développements en cours et à venir utilisant le LIP ouvrent de nouvelles perspectives quant à la mise au point de nouveaux outils pour le traitement d'images

Implementation of Spatial Domain Homomorphic Filtering on Embedded Mobile Devices

This paper describes the analysis of the Homomorphic filtering algorithm, the equivalency between the frequency and spatial-domain methods and the implementation of low-pass and high-pass spatial domain Homomorphic filter in low power embedded devices. It is shown that the Homomorphic filter in the spatial domain combines the sensitivity of local/neighbourhood operations in addition to Laplacian-type edge enhancement, averaging operation of illumination intensity estimation, in addition to dynamic range compression associated with frequency-domain Homomorphic filters. A qualitative and quantitative comparison of the image results confirms the validity of the theoretical approach and advantages for digital hardware implementation. The developed filters are implemented on a Java-enabled mobile phone and form a low cost embedded image processing enhancement system.http://dx.doi.org/10.4314/njt.v34i2.1

General Adaptive Neighborhood Image Processing for Biomedical Applications

In biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

International audienceThis chapter focused on image restoration of both topographical and textural information of an observed surface from a registered image sequence acquired by optical sectioning through the common concepts of Shape-From-Focus (SFF) and Extended Depth-of-Field (EDF). More particularly, the essential step of these complementary processes of restoration: the focus measurement, is examined. After a brief specialized review, we introduced novel evolved focus measurements that push the limits of state-of-the-art ones in terms of sensitivity and robustness, in order to cope with various frequently encountered acquisition issues