Efficiency of texture image enhancement by DCT-based filtering

International audienceTextures or high-detailed structures as well as image object shapes contain information that is widely exploited in pattern recognition and image classification. Noise can deteriorate these features and has to be removed. In this paper, we consider the influence of textural properties on efficiency of image enhancement by noise suppression for the posterior treatment. Among possible variants of denoising, filters based on discrete cosine transform known to be effective in removing additive white Gaussian noise are considered. It is shown that noise removal in texture images using the considered techniques can distort fine texture details. To detect such situations and to avoid texture degradation due to filtering, filtering efficiency predictors, including neural network based predictor, applicable to a wide class of images are proposed. These predictors use simple statistical parameters to estimate performance of the considered filters. Image enhancement is analysed in terms of both standard criteria and metrics of image visual quality for various scenarios of texture roughness and noise characteristics. The discrete cosine transform based filters are compared to several counterparts. Problems of noise removal in texture images are demonstrated for all of them. A special case of spatially correlated noise is considered as well. Potential efficiency of filtering is analysed for both studied noise models. It is shown that studied filters are close to the potential limits

Automatic Adaptive Lossy Compression of Multichannel Remote Sensing Images

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted

Estimation of Variance and Spatial Correlation Width for Fine-Scale Measurement Error in Digital Elevation Model

International audienceIn this article, we borrow from the blind noise parameter estimation (BNPE) methodology early developed in the image processing field an original and innovative no-reference approach to estimate digital elevation model (DEM) vertical error parameters without resorting to a reference DEM. The challenges associated with the proposed approach related to the physical nature of the error and its multifactor structure in DEM are discussed in detail. A suitable multivariate method is then developed for estimating the error in gridded DEM. It is built on a recently proposed vectorial BNPE method for estimating spatially correlated noise using noise informative areas and fractal Brownian motion. The new multivariate method is derived to estimate the effect of the stacking procedure and that of the epipolar line error on local (fine-scale) standard deviation and autocorrelation function width of photogrammetric DEM measurement error. Applying the new estimator to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM2 and Advanced Land Observing Satellite (ALOS) World 3D DEMs, good agreement of derived estimates with results available in the literature is evidenced. Adopted for TanDEM-X-DEM, estimates obtained agree well with the values provided in the height error map. In future works, the proposed no-reference method for analyzing DEM error can be extended to a larger number of predictors for accounting for other factors influencing remote sensing (RS) DEM accuracy

Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images

International audienceA maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7 x 7 x 7 spatial-spectral pixels. Each MSW is regarded as an additive mixture of spectrally correlated fractal Brownian motion (fBm)-samples and random noise. The main advantage of the proposed method is its ability to accurately estimate band noise variances locally by using spatial and spectral texture correlations from a single textural MSW. For each spectral band, both additive and signal-dependent band noise components are estimated by linear fit of local noise variances obtained from many MSWs distributed over the whole band intensity range. CRLB-based analysis of the estimator performance shows that a good compromise is to jointly process seven adjacent spectral bands. The proposed method performance is assessed first on synthetic fBm-data and on real images with synthesized noise. Finally, four different AVIRIS datasets from 1997 flying season are considered. Good coincidence between additive and signal-dependent AVIRIS random noise components estimates obtained by our method and the estimates retrieved from AVIRIS calibration data is demonstrated. These experiments suggest that it is worth taking into account noise signal-dependency hypothesis for processing AVIRIS data

Biodiversity screening of gut microbiome during the allogeneic hematopoietic stem cell transplantation: data from the real-life clinical practice

Biodiversity of a gut microbiome has been shown as an important predictor of transplant-related outcomes and infections in allogeneic hematopoietic stem cell transplantation (HSCT). We conducted a single-center real-life clinical study and implemented a routine gut microbiome diversity monitoring across the course of allogeneic HSCT. Twelve patients (with ALL, AML, CML, biphenotypic leukemia and aplastic anemia) were enrolled in a stool samples collection protocol before the start of HSCT and during a 30-day post-transplant period. We have shown the feasibility of a gut microbiome monitoring in a real-life clinical setting and have proven that the alpha-biodiversity of the microbiome is significantly reduced with HSCT in comparison with the individual patient baseline level (Xdc 72.93; p < 0.001; multivariate Dirichlet analysis), what may be related to the antibiotic use and conditioning regimen. Overall, the gut microbiome biodiversity monitoring may be clinically used in a real-life HSCT setting to identify the high-risk groups for developing bloodstream infections and transplant-related negative outcomes

Efficient Rotation-Scaling-Translation Parameter Estimation Based on the Fractal Image Model

International audienceThis paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator MLfBm (ML stands for "maximum likelihood" and fBm stands for "fractal Brownian motion") has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the MLfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75-2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for MLfBm estimates can be obtained from the Cramer-Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameter

Image Noise-Informative Map For Noise Standard Deviation Estimation

International audienceThe problem of automatic detection of image areas that can be reliably selected for accurate estimation of additive noise standard deviation (STD), irrespectively to processed image properties, is considered in this paper. For getting accurate estimate of either texture or noise parameters involved, we distinguish two complementary image informative maps: (1) noise-informative (NI) map and (2) its complementary texture-informative (TI) map. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in a single scanning window (SW). The TI map is simply evolved as the complementary part of N map currently updated. Final noise STD estimation is performed by efficient analysis of finite size 9x9 block DCT coefficients in NI SWs. Experiments on large image database have proved that the proposed approach outperforms state-of-the-art estimators with respect to both noise STD estimates bias and variance

Selection of a Similarity Measure Combination for a Wide Range of Multimodal Image Registration Cases

International audienceMany similarity measures (SMs) were proposed to measure the similarity between multimodal remote sensing (RS) images. Each SM is efficient to a different degree in different registration cases (we consider visible-to-infrared, visible-to-radar, visible-to-digital elevation model (DEM), and radar-to-DEM ones), but no SM was shown to outperform all other SMs in all cases. In this article, we investigate the possibility of deriving a more powerful SM by combining two or more existing SMs. This combined SM relies on a binary linear support vector machine (SVM) classifier trained using real RS images. In the general registration case, we order SMs according to their impact on the combined SM performance. The three most important SMs include two structural SMs based on modality independent neighborhood descriptor (MIND) and scale-invariant feature transform-octave (SIFT-OCT) descriptors and one area-based logarithmic likelihood ratio (logLR) SM: the former ones are more robust to structural changes of image intensity between registered modes, the latter one is to image noise. Importantly, we demonstrate that a single combined SM can be applied in the general case as well as in each particular considered registration case. As compared to existing multimodal SMs, the proposed combined SM [based on five existing SMs, namely, MIND, logLR, SIFT-OCT, phase correlation (PC), histogram of orientated phase congruency (HOPC)] increases the area under the curve (AUC) by from 1% to 21%. From a practical point of view, we demonstrate that complex multimodal image pairs can be successfully registered with the proposed combined SM, while existing single SMs fail to detect enough correspondences for registration. Our results demonstrate that MIND, SIFT, and logLR SMs capture essential aspects of the similarity between RS modes, and their properties are complementary for designing a new more efficient multimodal SM. Index Terms-Area-based similarity measure (SM), combined SM, linear binary classifier, multimodal image registration , remote sensing (RS), structural similarity, support vector machine (SVM)