Satellite image restoration in the context of a spatially varying point spread function

Dynamic distortion in resonant galvanometric optical scanners

High-speed optical systems are revolutionizing biomedical imaging in microscopy, DNA sequencing, and flow cytometry, as well as numerous other applications, including data storage, display technologies, printing, and autonomous vehicles. These systems often achieve the necessary imaging or sensing speed through the use of resonant galvanometric optical scanners. Here, we show that the optical performance of these devices suffers due to the dynamic mirror distortion that arises from the variation in torque with angular displacement. In one of two scanners tested, these distortions result in a variation of signal-to-noise (Strehl) ratio by an order of magnitude across the field of view, degrading transverse resolution by more than a factor of 2. This mirror distortion could be mitigated through the use of stiffer materials, such as beryllium or silicon carbide, at the expense of surface roughness, as these cannot be polished to the same degree of smoothness as common optical glasses. The repeatability of the dynamic distortion indicates that computational and optical corrective methods are also possible.

Fast image restoration for spatially varying defocus blur of imaging sensor

This paper presents a fast adaptive image restoration method for removing spatially varying out-of-focus blur of a general imaging sensor. After estimating the parameters of space-variant point-spread-function (PSF) using the derivative in each uniformly blurred region, the proposed method performs spatially adaptive image restoration by selecting the optimal restoration filter according to the estimated blur parameters. Each restoration filter is implemented in the form of a combination of multiple FIR filters, which guarantees the fast image restoration without the need of iterative or recursive processing. Experimental results show that the proposed method outperforms existing space-invariant restoration methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed to a wide area of image restoration applications, such as mobile imaging devices, robot vision, and satellite image processing.