Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods.

In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full resolution images results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images. In this paper, we propose a full-resolution training framework for deep learning-based pansharpening. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus avoiding any loss of information. To ensure spectral and spatial fidelity, a suitable two-component loss is defined. The spectral component enforces consistency between the pansharpened output and the low-resolution multispectral input. The spatial component, computed at high-resolution, maximizes the local correlation between each pansharpened band and the panchromatic input. At testing time, the target-adaptive operating modality is adopted, achieving good generalization with a limited computational overhead. Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework guarantee a pretty good performance in terms of both full-resolution numerical indexes and visual quality.

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.

We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.

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.