Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image

Total variation (TV) minimisation algorithms have been successfully applied in compressive sensing (CS) recovery for natural images owing to its advantage of preserving edges. However, traditional TV is no longer appropriate for omni-directional image processing because of the distortions in catadioptric imaging systems. The omni-gradient computing method combined with the characteristics of omni-directional imaging is proposed in this study. To reconstruct the image from its compressive samples, the omni-total variation (omni-TV) regularisation based on omni-gradient is utilised instead of traditional TV during the image restoration. The experimental results show that the omni-directional images can be reconstructed effectively and accurately. Compared with the classical TV minimisation model, the images recovered based on omni-TV model can provide higher quality both in subjective evaluation and objective evaluation.