Abstract
We propose a new method of image restoration for catadioptric defocus blur using omnitotal variation (Omni-TV) minimization based on omnigradient. Catadioptric omnidirectional imaging systems usually consist of conventional cameras and curved mirrors for capturing 360° field of view. The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are used. In an omnidirectional image, two points near each other may not be close to one another in the 3D scene. Traditional gradient computation cannot be directly applied to omnidirectional image processing. Thus, omnigradient computing method combined with the characteristics of catadioptric omnidirectional imaging is proposed. Following this Omni-TV minimization is used as the constraint for deconvolution regularization, leading to the restoration of defocus blur in an omnidirectional image to obtain all sharp omnidirectional images. The proposed method is important for improving catadioptric omnidirectional imaging quality and promoting applications in related fields like omnidirectional video and image processing.
Highlights
Owing to the advantage of one-shot seamless panoramic imaging, catadioptric omnidirectional imaging systems consisting of conventional cameras and curved mirrors for capturing 360∘ field of view are widely used in many vision applications, such as aerial photographic reconnaissance, intelligent transportation system, robot navigation, surveillance, medical applications, and video conferencing [1,2,3,4,5]
TwIST based on traditional Total Variation (TV) TwIST based on omnitotal variation (Omni-TV) Difference of signal-to-noise ratio (SNR) between the two (b) 30
A novel method of image restoration for catadioptric defocus blur image based on omnitotal variation minimization is proposed
Summary
Owing to the advantage of one-shot seamless panoramic imaging, catadioptric omnidirectional imaging systems consisting of conventional cameras and curved mirrors for capturing 360∘ field of view are widely used in many vision applications, such as aerial photographic reconnaissance, intelligent transportation system, robot navigation, surveillance, medical applications, and video conferencing [1,2,3,4,5]. To solve the defocus problem, the classical Wiener filter and Richardson-Lucy deconvolution method [6] can be applied to obtain the clear images. The TV minimization based regularization restoration approach aims to preserve more image details and restrain noise impact and ringing artifacts.
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