Objective performance evaluation of a moving object super-resolution system

We present evaluation of the performance of moving object super-resolution (MOSR) through objective image quality metrics. MOSR systems require detection, tracking, and local sub-pixel registration of objects of interest, prior to superresolution. Nevertheless, MOSR can provide additional information otherwise undetected in raw video. We measure the extent of this benefit through the following objective image quality metrics: (1) Modulation Transfer Function (MTF), (2) Subjective Quality Factor (SQF), (3) Image Quality from the Natural Scene (MITRE IQM), and (4) minimum resolvable Rayleigh distance (RD). We also study the impact of non-ideal factors, such as image noise, frame-to-frame jitter, and object rotation, upon this performance. To study these factors, we generated controlled sequences of synthetic images of targets moving against a random field. The targets exemplified aspects of the objective metrics, containing either horizontal, vertical, or circular sinusoidal gratings, or a field of impulses separated by varying distances. High-resolution sequences were rendered and then appropriately filtered assuming a circular aperture and square, filled collector prior to decimation. A fully implemented MOSR system was used to generate super-resolved images of the moving targets. The MTF, SQF, IQM, and RD measures were acquired from each of the high, low, and super-resolved image sequences, and indicate the objective benefit of super-resolution. To contrast with MOSR, the low-resolution sequences were also up-sampled in the Fourier domain, and the objective measures were collected for these Fourier up-sampled sequences, as well. Our study consisted of over 800 different sequences, representing various combinations of non-ideal factors.