Restoration of interlaced images degraded by variable velocity motion

Abstract

An interlaced composition of odd and even subimage fields is a very common video formation technique. Motion degradation is an inherent problem in portable imaging systems, such as airborne imaging, mobile phones, robots, etc. When relative motion between the interlacing camera and the scene occurs during imaging, two distortion types degrade the image: the edge ''staircase effect'' due to shifted appearances of objects in successive fields, and blur due to scene motion during each field exposure. In contrast to other previous works that dealt with only uniform velocity motion, here we consider a more general, realistic, and complicated case, in which the motion velocity is not necessarily uniform. The motion in each field and the displacement are assumed to be space invariant. Since conventional motion identification techniques used in other works cannot be employed in the case of variable velocity motion, a new method for identification of the motion from each field is used, and different point spread functions are identified for each field. The restored image is achieved by deblurring each field separately, and then realigning the fields. Results of motion identification and image restoration for various motion types are presented and analyzed for simulated and realdegraded images.

Initialization of iterative parametric algorithms for blind deconvolution of motion-blurred images

“…When no knowledge exists about the original image and about the image distortion properties, mathematical image quality measures are rarely used and have limited success because of the lack of knowledge about the desired properties of the restored image. [17][18][19] The cameraman image shown in Fig. 1 was used as the original image in the simulation examples presented in Figs.…”

“…The original image in most methods [7][8][9][10] is modeled and expressed by a 2-D homogeneous Gauss-Markov process represented by the following 2-D autoregressive model: 19) where a(k, l) are the image model coefficients, and S a is the causal (quarter-plane) 9 or the nonsymmetric half-plane 8 model support. The modeling error (i, j) is a zero-mean homogeneous Gaussian-distributed white-noise process with covariance Q ͑i, j͒ ϭ ␦͑i, j͒ and is independent of f(i, j).…”

Image restoration of hybrid time delay and integration camera system with residual motion

“…But as the residual motion usually is not a uniform linear motion, these blur kernels will change with motion trajectory. 13,14 Research focused on the TDI image motion deblurring is mainly based on particular structures. Hochman et al 15 proposed a method for restoration of the TDI blur images influenced by mechanical vibrations, and the TDI camera utilized in their system is staggered which is different from regular TDI camera and is often employed in thermal imaging.…”

Superresolution reconstruction of a video captured by a vibrated time delay and integration camera

“…Other techniques 18-21 did incorporate shiftvariant blur identification algorithms in their SR methods, but they are applicable only to blur types that can be characterized by a single parameter, such as an out of focus and a uniform-velocity motion. The blur resulting from lowfrequency vibrations ͑our case͒ has a randomly varying size and shape, depending on the initial time of exposure during the vibration period, [5][6][7][8]17 and consequently cannot be handled under their formulations.…”

“…Most of the researches that dealt with restoration of images degraded by vibrations considered mainly staring ͑nonscanning͒ cameras. [5][6][7][8] This paper addresses restoration of a video of a stationary scene, captured by a vibrated staggered TDI camera. As a result of the vibrations and the scanning mechanism just described, the resulting distortion is not shift-invariant but rather, it is spatially random according to the vibration spectrum, 9 hence, common shift-invariant image filtering restoration techniques are inappropriate.…”