A sensor-based approach to linear blur identification for real-time video enhancement

Abstract

Super-resolution (SR) methods are largely affected by the accurate evaluation of the Point Spread Function (PSF) that is related to the input frames. When the frames are degraded by heavy motion blur, the PSFs are highly non-isotropic, which further complicates their estimation. The ill-posed nature of blur identification is usually addressed using the assumption of linear and uniform motion. However, in real-life systems, this may deviate significantly from the actual motion blur. To resolve the above, this work proposes combining a scheme that validates the initial motion assumption with the real-time reconfiguration property of an adaptive image sensor. If the linearity and uniformity assumption is invalid for a given motion region, the sensor is locally reconfigured to larger pixels that produce higher frame-rate samples with reduced blur. Once the appropriate configuration that gives rise to a valid motion assumption is applied, highly accurate PSFs are estimated, resulting to an improved SR reconstruction quality.