Oren Haik

Blind restoration of atmospherically degraded images by automatic best step-edge detection

Image restoration algorithms often require previous knowledge about the point spread function (PSF) of the disturbance. Deriving the PSF manually from a degraded ideal step-edge in the image is a well known procedure intended mainly for isotropic degradations. A common image degradation that can be approximated as isotropic is the atmospheric blurring in long-distance imaging. This paper proposes an efficient method that automatically finds the best (closest to ideal) step-edge from the degraded image. The identified PSF is then used to restore the image. The existence of a good step-edge in the image may be assumed in cases such as imaging of urban areas, which is common in applications such as visual surveillance and reconnaissance. The criteria employed include the straightness and length of the edge, its strength, and the homogeneity of the step. An efficient algorithm is proposed, and results of automatic blind image restoration based on the automatically extracted PSF are shown.

Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere

Remotely sensed videos, captured by high-resolution imagers, are likely to be degraded by the atmosphere. In still images, the degradation sources, which include turbulence and aerosols, mainly cause blur. In video sequences, however, spatiotemporally varying distortions caused by turbulence also become important. These atmospheric degradations reduce image quality and therefore the ability of target acquisition by the observers. The effects of image quality and image restoration (deblurring) on target acquisition in still images were examined previously in several studies. Nevertheless, results obtained in static situations may not be appropriate for dynamic situations (with moving targets), which are frequently more realistic. This work examines the effect of image restoration on the ability of observers to acquire moving objects (such as humans and vehicles) in video sequences. This is done through perception experiments that compare acquisition probabilities in both restored and nonrestored video sequences captured by a remote-sensing thermal imaging system. Results show that image restoration can significantly improve the acquisition probability. These results correspond to the static case. However, unlike the static case, considerably smaller differences were obtained here between the probabilities of target detection and target recognition.

Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere

We aim to determine the effect of image restoration (deblurring) on the ability to acquire moving objects detected automatically from long-distance thermal video signals. This is done by first restoring the videos using a blind-deconvolution method developed recently, and then examining its effect on the geometrical features of automatically detected moving objects. Results show that for modern (low-noise and high-resolution) thermal imaging devices, the geometrical features obtained from the restored videos better resemble the true properties of the objects. These results correspond to a previous study, which demonstrated that image restoration can significantly improve the ability of human observers to acquire moving objects from long-range thermal videos.

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

Various applications such as industrial product inspec- tion or low signal-to-noise situations (as in thermal imaging) employ a time delay and integration (TDI) scanning imaging technique. Due to common vibration sources such as the camera platform motion or the thermal detectors cooling system, the acquired image may be degraded by severe shift-variant geometric distortions and motion blur. We use these vibrations in terms of superresolution to create an improved high-resolution video sequence from the degraded lower resolution sequence, in two main stages: subpixel motion es- timation with respect to translations and rotations, used for point spread function (PSF) estimation, followed by an efficient implemen- tation of the projection onto convex sets (POCS) method. We gen- eralize and considerably improve a previous technique for restora- tion of a single image captured by a translational vibrated staggered-TDI camera (Hochman et al., 2004). The proposed method is implemented with both simulated videos and real de- graded thermal videos. A comparative analysis shows an advantage of the proposed method over others in restoring the vibrated

Classification of moving objects in atmospherically degraded video

Abstract. Classification of moving objects in imaging through long-distance atmospheric path may be affected by distortions such as blur and spatiotemporal movements caused by air turbulence. This work aims to study and quantify the effects of these distortions on the ability to classify moving objects in atmospherically degraded video signals. For this purpose, we perform simulations and examine real long-range thermal video cases. In the simulation, we evaluate various geometrical (shape-based) object features for classification at different distortion levels. Furthermore, we examine the influence of image restoration on the classification performances in the real-degraded videos, using geometrical and textural features (combined and in separate) of the objects. Principal component analysis together with both k-nearest neighbor and support vector machines is used for the classification process. Results show how classification performances decrease as the level of blur increases, and how successful digital image restoration for real cases can significantly improve the classification performances.

Surveillance in long-distance turbulence-degraded videos

Surveillance in long-distance turbulence-degraded video is a difficult challenge because of the effects of the atmospheric turbulence that causes blur and random shifts in the image. As imaging distances increase, the degradation effects become more significant. This paper presents a method for surveillance in long-distance turbulence-degraded videos. This method is based on employing new criteria for discriminating true from false object detections. We employ an adaptive thresholding procedure for background subtraction, and implement new criteria for distinguishing true from false moving objects, that take into account the temporal consistency of both shape and motion properties. Results show successful detection also tracking of moving objects on challenging video sequences, which are significantly distorted with atmospheric turbulence. However, when the imaging distance is increased higher false alarms may occur. The method presented here is relatively efficient and has low complexity.

Classification of small moving objects in atmospherically degraded video

Acquisition and classification of moving objects in imaging through long-distance atmospheric path (more than 1-2 km) may be affected by distortions such as blur and spatiotemporal movements caused by air turbulence. These distortions are more meaningful when the size of the objects is relatively small (for instance, few pixels width). This work aims to study and quantify the effects of these distortions on the ability to classify small moving objects in atmosphericallydegraded video signals. For this purpose, moving objects were extracted from real video signals recorded through longdistance atmospheric path. Then, various geometrical and textural object features were extracted, and reduced to two principle components using principle component analysis (PCA). The effect of the atmospheric distortion on object classification was examined using support vector machine (SVM) classifier. Furthermore, the influence of image restoration on the classification performances was examined for the real-degraded videos. Results show how classification performances are decreasing when the images are degraded by the atmospheric path compared to the case where successful image restoration is performed.

Improvement of automatic acquisition of moving objects in long-distance imaging by blind image restoration

Automatic acquisition of moving objects from long-distance video sequence is a fundamental task in many applications such as surveillance and reconnaissance. However, the atmospheric degradations, which include blur and spatiotemporal-varying distortions, may reduce the quality of such videos, and therefore, the ability to acquire moving targets automatically. Pervious studies in the field of automatic acquisition of moving objects ignored the blur in the video frames. They usually employed simple methods for noise reduction (such as temporal and spatial smoothing) and motion compensation (registration of frames). The purpose of this work is to determine the effect of image restoration (de-blurring) on the ability to acquire moving objects (such as humans and vehicles) automatically. This is done here by first, restoring the long-distance thermal videos using a novel blind image deconvolution method developed recently, and then comparing the automatic acquisition capabilities in the restored videos versus the non-restored versions. Results show that image restoration can significantly improve the automatic acquisition capability. These results correspond to a previous study which demonstrated that image restoration can significantly improve the ability of human observers to acquire moving objects from a long-range thermal video.