Conventional X-band radars constitute a convenient cheap source of measurements for deterministic sea wave profiling and prediction. The radar backscatter measurements, however, suffer from several imperfections such as the effects of larger waves shadowing smaller waves. In order to extract the wave profiles and build a reliable (spectral) sea model, multiple radar scans must be processed. A technique based on 3DFFT has been proven to show promising results and has been implemented in commercially available wave profiling radars. Nonetheless, there are several drawbacks associated with using 3DFFT to determine the phase-resolved spectral coefficients in the sea model from a number of radar scans. In this article, we present an alternative spectral approach that can offer several advantages over 3DFFT in radar wave profiling and prediction. First, it has the ability to directly handle ship movement and radar rotation antenna effects with no approximation, interpolation, or extra cost. Second, it can offer an increase in the output update rate by eliminating repeating computations, which is achieved by its recursive nature (i.e., using past output) and by its multistep architecture. Third, it can be set to readily target the relative spectral components that can best describe the wave field. The proposed technique is evaluated using raw backscatter field data from a dedicated sea trial. A simulation study is also included to gain more understanding about the performance of the proposed technique in particular and radar wave sensing and prediction in general.

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, photo response nonuniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the “Dresden image database.”

Photo-response non-uniformity (PRNU) noise has been well established as a reliable fingerprint of imaging sensors for source camera identification and other forensic applications. In this paper, we introduce a novel denoising method for PRNU noise extraction that considerably outperforms the existing techniques. The rationale is to use as little as one adjacent pixel in spatial domain filtering to suppress the pixel-to-pixel correlation in the estimation of the (supposedly white) PRNU noise. Experimental results are presented based on an image database of ten cameras of different models and makes. These results are embodied in the receiver operating characteristic (ROC) of source camera identification.

We present a method for multidimensional Fourier transform estimation that is characterised by fast convergence rates using low sampling rates. Unlike similar methods reported in research literature, the proposed approach works efficiently even if the signal is bandpass or multiband. Therefore, such signals can be processed competently without downconversion or filtering. The performance of the developed method is analysed mathematically and illustrated by a numerical example.