Sampling strategy for the sparse recovery of infrared images.

The compressive sensing (CS) framework states that a signal that has a sparse representation in a known basis may be reconstructed from samples obtained at a sub-Nyquist sampling rate. The Fourier domain is widely used in CS applications due to its inherent properties. Sparse signal recovery applications using a small number of Fourier transform coefficients have made solutions to large-scale data recovery problems, including image recovery problems, more practical.

Detection and tracking of sea-surface targets in infrared and visual band videos using the bag-of-features technique with scale-invariant feature transform.

Sea-surface targets are automatically detected and tracked using the bag-of-features (BOF) technique with the scale-invariant feature transform (SIFT) in infrared (IR) and visual (VIS) band videos. Features corresponding to the sea-surface targets and background are first clustered using a training set offline, and these features are then used for online target detection using the BOF technique. The features corresponding to the targets are matched to those in the subsequent frame for target tracking purposes with a set of heuristic rules.

Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues.

Image enhancement is an important preprocessing step of infrared (IR) based target recognition and surveillance systems. For a better visualization of targets, it is vital to develop image enhancement techniques that increase the contrast between the target and background and emphasize the regions in the target while suppressing noises and background clutter. This study proposes what we believe to be a novel IR image enhancement method for sea-surface targets based on local frequency cues.