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Super-resolution (SR) is a well-studied problem in signal processing, particularly with regard to image and video applications. SR techniques are useful because unlike simple interpolation, they create a high-resolution signal from a low-resolution input by generating new information that was not previously present. A growing body of research shows progress in development of SR techniques using dictionary...
The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the field of digital image forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. In this work, we...
Nonnegative matrix factorization (NMF) is a widely-used tool for obtaining low-rank approximations of nonnegative data such as digital images, audio signals, textual data, financial data, and more. One disadvantage of the basic NMF formulation is its inability to control the amount of dependence among the learned dictionary atoms. Enforcing dependence within predetermined groups of atoms allows objects...
Dictionary learning through matrix factorization has become widely popular for performing music transcription and source separation. These methods learn a concise set of dictionary atoms which represent spectrograms of musical objects. However, there is no guarantee that the atoms learned will be perceptually meaningful, particularly when there exists significant spectral and temporal overlap among...
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