nils-herrmann/clean_scirepeval huggingface dataset:
Classification-based nonlocal SAR despeckling

Nonlocal techniques represent the current state of the art in SAR despeckling, providing a good compromise between speckle reduction and preservation of relevant image features. Nonetheless, they are not free from problems, going from the loss of image features to the introduction of their own brand of artifacts, due to the inability to deal equally well with all types of imaged scenes. A possible tool to improve performance is a prior segmentation or classification of the image, so as to adjust the filter parameters to fit the nature of the region under analysis. This work first provides some insight into the potential of classification-based nonlocal filtering by running simulation experiments in a controlled environment. Then proposes a new version of the SAR-BM3D despeckling technique in which each pixel is first classified as homogeneous or not, and then filtered with class-adapted parameters. Although results on real SAR images are still questionable, there is already some significant gain in selected areas that justifies the interest towards this approach.
Pruned tree-structured vector quantization of medical images with segmentation and improved prediction

The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to reserve bits for diagnostically relevant areas. Moreover, the authors improve the quality of prediction and encoding in two additional ways: by increasing the memory of the predictor itself and by using ridge regression for prediction. The improved encoding scheme was tested via computer simulations on a set of mediastinal CT scans; results are compared with those obtained using a more conventional scheme proposed recently in the literature. There were remarkable improvements in both the prediction accuracy and the encoding quality, above and beyond what comes from the segmentation. Test images were encoded at 0.5 bit per pixel and less without any visible degradation for the diagnostically relevant region.
Compression of multitemporal remote sensing images through Bayesian segmentation

Multitemporal remote sensing images are useful tools for many applications in natural resource management. Compression of this kind of data is an issue of interest, yet, only a few paper address it specifically, while general-purpose compression algorithms are not well suited to the problem, as they do not exploit the strong correlation among images of a multitemporal set of data. Here we propose a coding architecture for multitemporal images, which takes advantage of segmentation in order to compress data. Segmentation subdivides images into homogeneous regions, which can be efficiently and independently encoded. Moreover this architecture provides the user with a great flexibility in transmitting and retrieving only data of interest
SAR despeckling based on soft classification

We propose a new approach to SAR despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixel-wise classification of the image, one can take advantage of this diversity by selecting the more appropriate combination of estimators for each image region. We implement a simplified version of this approach, using soft classification and two state-of-the-art despeckling tools, with opposite properties, as basic estimators. Experiments on real-world high-resolution SAR images prove the effectiveness of the proposed technique and confirm the potential of the whole approach.
A SPIHT-like image coder based on the contourlet transform

The contourlet transform was recently proposed to overcome the limited ability of wavelet to represent image edges and discontinuities. Besides retaining the desirable characteristics of wavelet transform, such as multiresolution and localization, it has two additional important features: directionality and anisotropy. In this work we propose a new image coding technique based on an hybrid contourlet-wavelet decomposition. The encoder builds upon the well-known SPIHT algorithm, which is suitably modified to take into account the new hierarchical structure of the transform coefficients and the correlation between them. Both numerical results and visual quality confirm the potential of this approach, especially for images with high texture content.
An interference-tolerant algorithm for localization of cyclostationary-signal sources

A method for detecting the number of cyclostationary signals radiated by remote sources and for estimating their directions of arrival by a linear and uniform array is presented. Whereas the traditional techniques exploit the spatial coherent properties, the new method locates the signal sources using the spectral coherence properties as well. This approach eliminates the need to know the characteristics of the noise and the interference, regardless of the extent of their spectral overlap. Moreover, the method applied equally well to environments containing more interferers than sensors. The conditions of applicability of the method are the existence and the knowledge of a cycle frequency at which all the signal sources exhibit spectral correlation but the noise and interference signals do not, and the existence and the knowledge of a value of the lag parameter such that the cyclic cross-correlation matrix of the desired signals has full rank.
Fusion of sar-optical data for land cover monitoring

This work deals with the fusion of SAR and optical data for land cover monitoring. We first propose to use co-registered optical data as a guide for nonlocal SAR image despeckling. Then, we fuse filtered Sentinel-1 SAR data with optical Sentinel-2 data for land-use classification. Experiments show that using optical-driven despeckled SAR data largely improves classification accuracy w.r.t using the original data and even multitemporal filtered data.
Texture-Based Segmentation of Very High Resolution Remote-Sensing Images

Segmentation of very high resolution remote-sensing images cannot rely only on spectral information, quite limited here for technological reasons, but must take into account also the rich textural information available. To this end, we proposed recently the Texture Fragmentation and Reconstruction (TFR) algorithm, based on a split-and-merge paradigm, which provides a sequence of nested segmentation maps, at various scales of observation. Early experiments on several high-resolution test images confirm the potential of TFR, but there is room for further improvements under various points of view. In this paper we describe the TFR algorithm and, starting from the analysis of some critical results propose two new version that address and solve some of its weak points.
InSAR-BM3D: A Nonlocal Filter for SAR Interferometric Phase Restoration

The block-matching 3-D (BM3D) algorithm, based on the nonlocal approach, is one of the most effective methods to date for additive white Gaussian noise image denoising. Likewise, its extension to synthetic aperture radar (SAR) amplitude images, SAR-BM3D, is a state-of-the-art SAR despeckling algorithm. In this paper, we further extend BM3D to address the restoration of SAR interferometric phase images. While keeping the general structure of BM3D, its processing steps are modified to take into account the peculiarities of the SAR interferometry signal. Experiments on simulated and real-world Tandem-X SAR interferometric pairs prove the effectiveness of the proposed method.
Guided filtering for PRNU-based localization of small-size image forgeries

PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithm's ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.
A fully convolutional neural network for low-complexity single-stage ship detection in Sentinel-1 SAR images

Ship detection is a fundamental task for SAR-based maritime surveillance. Besides providing high reliability, a good detector is required to be computationally light, in order to analyze huge areas in a reasonable time. We propose a fully convolutional neural network for ship detection in SAR images. Thanks to a relatively simple architecture, complexity remains low enough to allow for a single-stage approach, thus avoiding the possible errors of CFAR pre-screening. Experiments on a Sentinel-1 dataset prove the proposed CNN to be much more reliable than CFAR detection.
A Reliable Order-Statistics-Based Approximate Nearest Neighbor Search Algorithm

We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors. Data vectors are classified based on the index and sign of their largest components, thereby partitioning the space in a number of cones centered in the origin. The query is itself classified, and the search starts from the selected cone and proceeds to neighboring ones. Overall, the proposed algorithm corresponds to locality sensitive hashing in the space of directions, with hashing based on the order of components. Thanks to the statistical features emerging through ordering, it deals very well with the challenging case of unstructured data, and is a valuable building block for more complex techniques dealing with structured data. Experiments on both simulated and real-world data prove the proposed algorithm to provide a state-of-the-art performance.
Generalized-cost-measure-based address-predictive vector quantization

Address-predictive vector quantization (APVQ) exploits the interblock dependency by jointly encoding the addresses of the codewords associated with spatially close blocks. It profiles the same image quality as memoryless VQ for a much lesser bit rate (BR) and the same computational complexity. In the generalized-cost-measure-based APVQ, the two steps of the encoding process, namely, VQ and predictive address encoding, are carried out jointly by minimizing a generalized cost measure, which takes into account both the BR and the distortion. Computer simulations show that a significant improvement can be obtained with respect to APVQ in terms of both BR and distortion. Compared with memoryless VQ, a bit-rate reduction of almost 60% is obtained for the same image quality.
PRNU-based forgery detection with regularity constraints and global optimization

Detection of image forgeries is an important topic for forensics applications. One of the most interesting approaches to forgery detection relies on the photo-response non uniformity noise (PRNU), that can be considered as a sort of camera fingerprint and used as such to accomplish forgery detection. In fact, while genuine parts of an image exhibit the camera PRNU, this is not present in tampered areas. In this work, we present a new method to detect forgeries by using PRNU. In particular, we propose a minimum-risk Bayesian classification, aimed at minimizing the probability of error or, more in general, a weighted average of the two types of errors (false alarm, missing detection) according to their importance for the application. Then, we introduce a regularization term in the decision process to take into account prior information on the classification map. This step weights optimally the observed data and the regularity constraints to minimize the Bayesian risk. Since the regularization term is based on spatial properties of the decision map, classification cannot work on each pixel individually but must be carried out jointly on the whole image. The ensuing problem is NP-hard but we use relaxation and convex optimization techniques, based on proximal methods, to obtain a global optimum solution in limited time. Preliminary experiments with digital forgeries of different sizes and shapes prove that the improved technique provides a significant performance gain w.r.t. the original, at the cost of a limited increase in complexity.
Fingerprint liveness detection based on Weber Local image Descriptor

In this paper, we investigate the use of a local discriminative feature space for fingerprint liveness detection. In particular, we rely on the Weber Local Descriptor (WLD), which is a powerful and robust descriptor recently proposed for texture classification. Inspired by Weber's law, it consists of two components, differential excitation and orientation, evaluated for each pixel of the image. Joint histograms of these components are then processed to build the discriminative features used to train a linear kernel SVM classifier. Experimental results with different databases and different sensors show WLD to perform favorably compared to the state-of-the-art methods in fingerprint liveness detection. In addition, by combining WLD with LPQ (Local Phase Quantization) results further improve significantly.
Nonlocal Adaptive Multilooking in SAR Multipass Differential Interferometry

Filtering of interferometric synthetic aperture radar (InSAR) data has been the object of a growing number of papers, in recent years, aimed at improving the quality of differential InSAR (DInSAR) and tomography products based on multibaseline/multitemporal data. In this work, we investigate the potential of nonlocal adaptive multilooking (AML) techniques for the filtering of interferometric stacks. Nonlocal filtering has been already successfully applied to SAR data in many contexts, including the restoration of single interferograms. We propose several workflows, with different solutions for the data preprocessing and for the estimation of similarity, at the core of all nonlocal methods. Experiments on simulated and real data, the latter both at low and high spatial resolution, prove the proposed methods to provide significant improvements over nonadaptive multilooking in terms of both resolution and detail preservation, allowing for the generation of better interferometric products.
Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization

There is an ever-growing interest in the compression of SAR data because of the huge resources required for storage and transmission. This is especially true for spaceborne sensors, given the limited capacity of the downlink channel. Unfortunately, SAR data lack the useful properties on which compression algorithms rely; indeed, these are present in the focused images, but focusing is too complex for on-board implementation at this time. Poggi et al. (2000) proposed to perform on the satellite only the low-complexity range focusing, which increases the data correlation and better concentrates their energy. These properties were then exploited by adopting a variable-rate vector quantizer, with a clear performance improvement with respect to reference techniques. However, vector quantization (VQ) is too complex for actual on-board implementation, and therefore, here we replace VQ with trellis-coded VQ. To limit complexity, only small vectors are used, which reduces VQ's ability to exploit data dependencies; on the other hand, trellis coding allows one to encode large blocks of data at once, and to obtain a better partition of the input space. Experiments on real SAR data show that the overall performance is comparable to that of Poggi et al., but the complexity is much lower, making on-board implementation possible.
Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images

A new technique for the segmentation of single- and multiresolution (MR) remote sensing images is proposed. To guarantee the preservation of details at fine scales, edge-based watershed is used, with automatically generated markers that help in limiting oversegmentation. For MR images, the panchromatic and multispectral components are processed independently, extracting both the edge maps and the morphological and spectral markers that are eventually fused at the highest resolution, thus avoiding any information loss induced by pansharpening. Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.
A feature-based approach for image tampering detection and localization

We propose a new camera-based technique for tampering localization. A large number of blocks are extracted off-line from training images and characterized through features based on a dense local descriptor. A multidimensional Gaussian model is then fit to the training features. In the testing phase, the image is analyzed in sliding-window modality: for each block, the log-likelihood of the associated feature is computed, reprojected in the image domain, and aggregated, so as to form a smooth decision map. Eventually, the tampering is localized by simple thresholding. Experiments carried out in a number of situation of interest show promising results.
Sigmoid shrinkage for BM3D denoising algorithm

In this work we propose a modified version of the BM3D algorithm recently introduced by Dabov et al.  for the denoising of images corrupted by additive white Gaussian noise. The original technique performs a multipoint filtering, where the nonlocal approach is combined with the wavelet shrinkage of a 3D cube composed by similar patches collected by means of block-matching. Our improvement concerns the thresholding of wavelet coefficients, which are subject to a different shrinkage depending on their level of sparsity. The modified algorithm is more robust with respect to block matching errors, especially when noise is high, as proved by experimental results on a large set of natural images.
MAP-MRF filtering of SAR interferometric phase fields

Presents a new technique, based on a MAP (maximum a posteriori probability) approach, for filtering interferometric phase images. To describe prior knowledge about the unknown image, a MRF/Gibbs model is adopted, and its energy function is defined so as to impose two desirable constraints on the solution: the absence of residues (which leads to unambiguous phase unwrapping) and the smoothness of the phase field (which reduces noise). MAP estimation is carried out by simulated annealing, obtaining accurate results even for low values of the coherence.
Hierarchical Mrf-Based Segmentation of Remote-Sensing Images

Remote-sensing images are often composed by a hierarchy of nested regions, with complex regions that are regarded as homogeneous at some observation scale, but can be further segmented at finer scales. Tree-structured Markov random fields (TS-MRF) allow one to model such images, and to develop efficient segmentation algorithms for them. TS-MRF are traditionally based on binary trees of classes, but the use of generic trees, with more degrees of freedom, can likely provide a better performance, as was shown with reference to synthetic images. Here we build upon the ideas proposed to devise a segmentation algorithm that works effectively, and with a limited computational burden, on real-world remote sensing images.
Scattering-Based SARBM3D

Interpreting synthetic aperture radar (SAR) images may be a very challenging task, even for expert users. One of the main reasons is the multiplicative speckle noise typical of coherent acquisition systems. Therefore, despeckling can be expected to play a key role in the full exploitation of SAR imagery potential. However, even state-of-the-art despeckling algorithms neglect the physical phenomena hidden behind SAR imagery. Image acquisition depends on electromagnetic scattering, which is also at the basis of speckle noise. Taking into account scattering issues into more physical-based despeckling algorithms may only benefit the overall performance. In this paper, we propose a scattering-based (SB) version of the SAR block-matching 3D (BM3D) filter, named SB-SARBM3D. SARBM3D can be arguably considered as one of the most promising and accurate despeckling algorithms, providing a good compromise between speckle reduction and detail preservation. We modify the original algorithm so as to exploit the prior information available on the imaged scene, taken into account based on scattering concepts. The new algorithm is tested in a variety of different and complementary simulated scenarios, and its performance is assessed objectively by means of numerous synthetic parameters. Moreover, comparison with different state-of-the-art despeckling algorithms is performed on some actual SAR images, both inherent to natural and urbanized areas, for subjective evaluation. Thanks to the prior information, SB-SARBM3D outperforms the original algorithm in terms of both speckle reduction and detail preservation. Moreover, it reduces the annoying artifacts introduced sometimes by SARBM3D in homogeneous areas of the image.
Fast Adaptive Nonlocal SAR Despeckling

Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.
Multispectral-image coding by spectral classification

This paper addresses the problem of multispectral-image compression and proposes a new encoding scheme, based on the classification of the multispectral image into regions of homogeneous land cover. Separate regions resulting from classification are efficiently encoded by means of conventional transform coding techniques, whereas the classification information is compacted resorting to spatial prediction and Ziv-Lempel coding. Simulation results show that the proposed algorithm assures both a high compression ratio and a good reproduction quality, with a reasonable computational complexity.