Statistics-based filtering for low signal-to-noise ratios, applied to rocket plume imaging

Extracting information from low signal to noise ratio images poses significant challenges. Noise makes extracting spatial features difficult, in particular if extraction of both large, smooth features at the same time as point-like features is required. This work describes a new statistical approach, able to handle both simultaneously, with the capacity of handling both positive and negative contrast signatures. The basic idea in this approach is that each pixel value can represent underlying statistics to a varying degree, depending on how similar it is to samples taken close to it, spatially and/or temporally. If the sample is similar to its surroundings, it is strongly filtered and also affects the filtering of neighboring samples, but if it is significantly different, it will remain largely unfiltered and does not influence neighboring pixel filtering. Simulations show that the filtering maintains energy conservation, significantly limits noise and at the same time maintains signal integrity. The filter is found to adapt to noise characteristics and spatiotemporal variations of the background. The technique is found to be well suited to rocket plume imaging, but is adaptable to a broad range of other applications.