Restoration methods, such as super-resolution (SR), largely depend on the accuracy of the point spread function (PSF). PSF estimation is an ill-posed problem, and a linear and uniform motion is often assumed. In real-life systems, this may deviate significantly from the actual motion, impairing subsequent restoration. To address the above, this work proposes a dynamically configurable imaging system that combines algorithmic video enhancement, field programmable gate array (FPGA)-based video processing and adaptive image sensor technology. Specifically, a joint blur identification and validation (BIV) scheme is proposed, which validates the initial linear and uniform motion assumption. For the cases that significantly deviate from that assumption, the real-time reconfiguration property of an adaptive image sensor is utilised, and the sensor is locally reconfigured to larger pixels that produce higher frame-rate samples with reduced blur. Results demonstrate that once the sensor reconfiguration gives rise to a valid motion assumption, highly accurate PSFs are estimated, resulting in improved SR reconstruction quality. To enable real-time reconstruction, an FPGA-based BIV architecture is proposed. The system's throughput is significantly higher than 25 fps, for frame sizes up to 1024 × 1024, and its performance is robust to noise for signal-to-noise ratio (SNR) as low as 20 dB.