nils-herrmann/clean_scirepeval huggingface dataset:
Blind PRNU-Based Image Clustering for Source Identification

We address the problem of clustering a set of images, according to their source device, in the absence of any prior information. Image similarity is computed based on noise residuals, regarded as single-image estimates of the camera’s photo-response non-uniformity (PRNU) pattern. First, residuals are grouped by correlation clustering, and several alternative data partitions are computed as a function of a running decision boundary. Then, these partitions are processed jointly to extract a single, more reliable, consensus clustering and, with it, more reliable PRNU estimates. Finally, both clustering and PRNU estimates are progressively refined by merging pairs of the same-PRNU clusters, selected on the basis of a maximum-likelihood ratio statistic. Extensive experiments prove the proposed method to outperform the current state of the art both on pristine images and compressed images downloaded from social networks. A remarkable feature of the method is that it does not require the user to set any parameter, nor to provide a training set to estimate them. Moreover, through a suitable choice of basic tools, and efficient implementation, complexity remains always quite limited.
Multispectral-image compression based on tree-structured Markov random field segmentation and transform coding

This paper presents a new compression technique for multispectral images. The proposed encoding algorithm is based on two steps: segmentation and transform coding. The segmentation step is based on a hierarchical tree-structured Markov random field model for the image, which is able to effectively take into account the spatial dependencies. After segmentation, class-adapted transform coding is used to decorrelate information both in the spectral and spatial domain. Simulation results show that the proposed technique exhibits a significant performance gain at very low bit rates, while assuring a satisfactory image quality.
A Bayesian approach for SAR interferometric phase restoration

The phase unwrapping problem for Interferometric SAR (InSar) has been approached with a number of local and global methods, to cope with the intrinsic noise due to the SAR image generation. The authors propose a new filtering technique for interferometric phase images, based on a Bayesian approach, such that the subsequent unwrapping can be performed by a path independent integration. Assuming that the original phase profile has bounded differences, they introduce an a priori Gibbs distribution for the data that encourages images with conservative phase gradient. The resulting maximum a posteriori estimation can be recast as a maximum likelihood optimization with a zero-residue constraint. The simulations on synthetic data, carried out by a Metropolis version of the simulated annealing algorithm, show satisfactory results as the restored phase profiles are very accurate even for low values of the coherence.
A comparison of flat and object-based transform coding techniques for the compression of multispectral images

In this work we implement and compare several state-of-the-art transform coding schemes for the compression of multispectral images, in order to better understand which elements have a deeper impact on the overall performance, and which tools guarantee the best results. All schemes are based on Karhunen-Loeve transform and/or wavelet transform, in various combinations, and use SPIHT as the coding engine. Moreover, besides the ordinary techniques, their object-based counterparts are also examined, so as to study the viability of such approach [M. Cagnazzo et al., Oct 2004] for these images. Whenever possible, an optimal rate allocation strategy is applied. The experiments, performed on images acquired by two different sensors, highlight the superiority of KLT as spectral transform; the rough equivalence between object-based and ordinary techniques in terms of rate-distortion performance; and the importance of the optimal allocation.
Biometric Spoofing Detection by a Domain-Aware Convolutional Neural Network

Biometric authentication systems are pervasive in modern society, but they are quite vulnerable to spoofing attacks. Research on spoofing (or liveness) detection is therefore very active. A number of methods have been proposed in the literature, sometimes with very promising results, but limited robustness with respect to the large variety of biometric traits, sensors, and attacks encountered in real-life. Recently, methods based on Convolutional Neural Networks (CNNs) are drawing great attention, given their success in many other image processing tasks. However, despite some promising results, they seem to suffer the same robustness problem, requiring heavy training to work properly. Here, we propose a new CNN architecture for biometric spoofing detection. Thanks to domain-specific knowledge, accounted for through a suitable loss function, a compact architecture is obtained, allowing reliable training also in the presence of small-size datasets. Experiments prove the proposal to provide state-of-art performance on several widespread datasets for face and iris liveness detection.
Improved tree-structured segmentation of remote sensing images

The Bayesian/MRF approach guarantees high-quality image segmentation, at the price of a significant computational cost. To limit complexity, one can resort to the recently proposed tree-structured MRF-based segmentation, which converts a Kclass problem in a sequence of much simpler binary tasks. The binary-tree structure imposed to the image, however, can also reduce segmentation accuracy. Here we propose an improved tree-structured segmentation algorithm, where disjoint regions, even belonging to the same class, are immediately split, giving rise to a generic tree structure, and are grouped again in classes only at the end of the algorithm. As a result, both segmentation speed and accuracy increase.
A Bayesian filtering technique for SAR interferometric phase fields

SAR interferograms are affected by a strong noise component which often prevents correct phase unwrapping and always impairs the phase reconstruction accuracy. To obtain satisfactory performance, most filtering techniques exploit prior information by means of ad hoc, empirical strategies. In this paper, we recast phase filtering as a Bayesian estimation problem in which the image prior is modeled as a suitable Markov random field, and the filtered phase field is the configuration with maximum a posteriori probability. Assuming the image to be residue free and generally smooth, a two-component MRF model is adopted, where the first component penalizes residues, while the second one penalizes discontinuities. Constrained simulated annealing is then used to find the optimal solution. The experimental analysis shows that, by gradually adjusting the MRF parameters, the algorithm filters out most of the high-frequency noise and, in the limit, eliminates all residues, allowing for a trivial phase unwrapping. Given a limited processing time, the algorithm is still able to eliminate most residues, paving the way for the successful use of any subsequent phase unwrapping technique.
Dynamic segmentation for image information mining

Information mining systems typically do not carry out image segmentation because a single algorithm could never perform well on the wide variety of sources and user applications encountered in practice. On the other hand, a large number of tools have been proposed in the literature that handle specific segmentation tasks very well. Dynamic segmentation is a possible solution, where the image is split recursively, in a hierarchical fashion, and different tools are used at each step to address specific segmentation tasks. In this work, the segmentation of a high-resolution test image is used as a running example and as a proof of concept of the potential of this approach.
Codebook ordering techniques for address-predictive VQ

The authors have investigated several algorithms aimed at either generating an ordered VQ (vector quantization) codebook or putting in order an already existing one, so that subsequent application of APVQ (address predictive VQ) or L-APVQ (lossy APVQ) will provide good results. In general, it appears that, as long as a reasonably ordered codebook is available, good performance is always obtained and little can be gained by the use of a more sophisticated algorithm. Therefore, a fast technique, like NMA (nearest merger algorithm), FLA (farthest insertion algorithm), or even the trivial MVA (maximum variance algorithm) (limited to lossless APVQ), appears to be a sensible choice. If CPU time is not a concern, however, the BSA (binary switching algorithm), properly tuned for the specific application, can make it possible to squeeze out some more bits in encoding. If a new codebook needs to be generated, the Kohonen algorithm is probably worth using due to its wide range, good performance and robustness.<<ETX>>
Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform

In this letter, we improve the class-based transform-coding scheme proposed by Gelli and Poggi for the compression of multispectral images. The original spatial-coding tools, 1-D discrete cosine transform and scalar quantization, are replaced by shape-adaptive wavelet transform and set partitioning in hierarchical trees. Numerical experiments show that the improved technique outperforms the original one for medium- to high-quality compression and is consistently superior to all reference techniques.
Supervised segmentation of remote sensing images based on a tree-structured MRF model

Most remote sensing images exhibit a clear hierarchical structure which can be taken into account by defining a suitable model for the unknown segmentation map. To this end, one can resort to the tree-structured Markov random field (MRF) model, which describes a K-ary field by means of a sequence of binary MRFs, each one corresponding to a node in the tree. Here we propose to use the tree-structured MRF model for supervised segmentation. The prior knowledge on the number of classes and their statistical features allows us to generalize the model so that the binary MRFs associated with the nodes can be adapted freely, together with their local parameters, to better fit the data. In addition, it allows us to define a suitable likelihood term to be coupled with the TS-MRF prior so as to obtain a precise global model of the image. Given the complete model, a recursive supervised segmentation algorithm is easily defined. Experiments on a test SPOT image prove the superior performance of the proposed algorithm with respect to other comparable MRF-based or variational algorithms.
A PatchMatch-Based Dense-Field Algorithm for Video Copy–Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.
Hierarchical Texture-Based Segmentation of Multiresolution Remote-Sensing Images

In this paper, we propose a new algorithm for the segmentation of multiresolution remote-sensing images, which fits into the general split-and-merge paradigm. The splitting phase singles out clusters of connected regions that share the same spatial and spectral characteristics. These clusters are then regarded as atomic elements of more complex structures, particularly textures, that are gradually retrieved during the merging phase. The whole process is based on a recently developed hierarchical model of the image, which accurately describes its textural properties. In order to reduce the computational burden and preserve contours at the highest spatial definition, the algorithm works on the high-resolution panchromatic data first, using low-resolution full spectral information only at a later stage to refine the segmentation. It is completely unsupervised, with just a few parameters set at the beginning, and its final product is not a single segmentation map but rather a sequence of nested maps which provide a hierarchical description of the image, at various scales of observations. The first experimental results, obtained on a remote-sensing Ikonos image, are very encouraging and confirm the algorithm potential.
A tree-structured Markov random field model for Bayesian image segmentation

We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.
Morphological road segmentation in urban areas from high resolution satellite images

High resolution satellite images provided by the last generation sensors significantly increased the potential of almost all the image information mining (IIM) applications related to earth observation. This is especially true for the extraction of road information, task of primary interest for many remote sensing applications, which scope is more and more extended to complex urban scenarios thanks to the availability of highly detailed images. This context is particularly challenging due to such factors as the variability of road visual appearence and the occlusions from entities like trees, cars and shadows. On the other hand, the peculiar geometry and morphology of man-made structures, particularly relevant in urban areas, is enhanced in high resolution images, making this kind of information especially useful for road detection. In this work, we provide a new insight on the use of morphological image analysis for road extraction in complex urban scenarios, and propose a technique for road segmentation that only relies on this domain. The keypoint of the technique is the use of skeletons as powerful descriptors for road objects: the proposed method is based on an ad-hoc skeletonization procedure that enhances the linear structure of road segments, and extracts road objects by first detecting their skeletons and then associating each of them with a region of the image. Experimental results are presented on two different high resolution satellite images of urban areas.
A marker-controlled watershed segmentation: Edge, mark and fill

The segmentation of very high resolution (VHR) images portraying complex urban scenarios is a rather challenging problem. In particular, great attention must be devoted to preserve fine man-made details, of major interest for most user applications. For this reason, edge-based segmentation methods are likely preferable to region-based methods. The latter, in fact, e.g. , , succeed in taking into account long range interactions and hence perform typically well in terms of “global” accuracy, but exhibit a lower “local” accuracy with respect to former, .
On-board compression of SAR data through range focusing

To reduce the bandwidth of the downlink channel in spaceborne SAR systems some form of data compression is required. Compressing the raw data is very inefficient because these data exhibit little or no correlation; on the other hand, data focusing is too demanding to be carried out on-board. The authors propose to carry out a partial, low-complexity focusing on-board, in order to recover part of the original dependencies, and then to compress the resulting data by means of a variable-rate adaptive vector quantization algorithm. Experiments show a performance improvement of 0.5-0.7 dB over comparable compression techniques.
Copy-move forgery detection based on PatchMatch

In this work we propose a new algorithm for copy-move forgery detection and localization, based on the fast computation of a dense nearest-neighbor field. To this end, we use PatchMatch, an iterative randomized algorithm for nearest-neighbor search, which exploits the regularity of natural images to converge very rapidly to a near-optimal and smooth field. We modify the basic algorithm to gain robustness against rotations, while keeping the original computational efficiency. Experimental results show the proposed technique to outperform almost uniformly all tested reference techniques in terms of both accuracy and speed.
A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection

Graphics editing programs of the last generation provide ever more powerful tools, which allow for the retouching of digital images leaving little or no traces of tampering. The reliable detection of image forgeries requires, therefore, a battery of complementary tools that exploit different image properties. Techniques based on the photo-response non-uniformity (PRNU) noise are among the most valuable such tools, since they do not detect the inserted object but rather the absence of the camera PRNU, a sort of camera fingerprint, dealing successfully with forgeries that elude most other detection strategies. In this paper, we propose a new approach to detect image forgeries using sensor pattern noise. Casting the problem in terms of Bayesian estimation, we use a suitable Markov random field prior to model the strong spatial dependences of the source, and take decisions jointly on the whole image rather than individually for each pixel. Modern convex optimization techniques are then adopted to achieve a globally optimal solution and the PRNU estimation is improved by resorting to nonlocal denoising. Large-scale experiments on simulated and real forgeries show that the proposed technique largely improves upon the current state of the art, and that it can be applied with success to a wide range of practical situations.
An Investigation of Local Descriptors for Biometric Spoofing Detection

Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance and flexibility. This paper aims at assessing the potential of these descriptors for the liveness detection task in authentication systems based on various biometric traits: fingerprint, iris, and face. Besides compact descriptors based on the independent quantization of features, already considered for some liveness detection tasks, we will study promising descriptors based on the joint quantization of rich local features. The experimental analysis, conducted on publicly available data sets and in fully reproducible modality, confirms the potential of these tools for biometric applications, and points out possible lines of development toward further improvements.
Benchmarking Framework for SAR Despeckling

Objective performance assessment is a key enabling factor for the development of better and better image processing algorithms. In synthetic aperture radar (SAR) despeckling, however, the lack of speckle-free images precludes the use of reliable full-reference measures, leaving the comparison among competing techniques on shaky bases. In this paper, we propose a new framework for the objective (quantitative) assessment of SAR despeckling techniques, based on simulation of SAR images relevant to canonical scenes. Each image is generated using a complete SAR simulator that includes proper physical models for the sensed surface, the scattering, and the radar operational mode. Therefore, in the limits of the simulation models, the employed simulation procedure generates reliable and meaningful SAR images with controllable parameters. Through simulating multiple SAR images as different instances relevant to the same scene we can therefore obtain, a true multilook full-resolution SAR image, with an arbitrary number of looks, thus generating (by definition) the closest object to a clean reference image. Based on this concept, we build a full performance assessment framework by choosing a suitable set of canonical scenes and corresponding objective measures on the SAR images that consider speckle suppression and feature preservation. We test our framework by studying the performance of a representative set of actual despeckling algorithms; we verify that the quantitative indications given by numerical measures are always fully consistent with the rationale specific of each despeckling technique, strongly agrees with qualitative (expert) visual inspections, and provide insight into SAR despeckling approaches.
Self-organizing codebooks for trellis-coded VQ

Because of its computational complexity, vector quantization (VQ) can work only on relatively small vectors, which severely limits its encoding performance. Trellis-coded VQ (TCVQ) circumvents this problem by using VQ in combination with a trellis encoding strategy, thus treating very large blocks of data at once. TCVQ is based on a size-N VQ codebook which is recursively partitioned to form two (possibly nearly optimal) codebooks of size N/2, four of size N/4, etc. Here we show that such a tree of codebooks can be easily designed, without the need of any postprocessing, by means of the Kohonen algorithm. Numerical experiments show the effectiveness of the proposed approach.
Optical-Driven Nonlocal SAR Despeckling

We propose a new synthetic aperture radar (SAR) despeckling technique based on nonlocal filtering and driven by a coregistered optical image. A preliminary homogeneous versus heterogeneous classification of the image is used to decide where the optical guide can be safely used, thus preventing any distortion of the SAR geometry. Even in regions where the use of optical data is enabled, despeckling is carried out exclusively in the SAR domain, and the optical guide is used only to improve the predictor selection in nonlocal filtering and, hence, in the estimation process. Experiments on real-world imagery confirm the potential of the proposed approach.
Image segmentation by tree-structured Markov random fields

We propose a new algorithm, based on a tree-structured Markov random field (MRP) model, to carry out the unsupervised classification of images. It presents several appealing features; due to the MRF model, it takes into account spatial dependencies, yet is computationally light because only binary MRFs are used and a progressive refinement of information takes place. Moreover, it is adaptive to the local characteristics of the image and provides useful side information about the segmentation process.