Projective image restoration using sparsity regularization

This paper presents a method of image restoration for projective ground images which lie on a projection orthogonal to the camera axis. The ground images are initially transformed using homography, and then the proposed image restoration is applied. The process is performed in the dual-tree complex wavelet transform domain in conjunction with L0 reweighting and L2 minimisation (L₀RL₂) employed to solve this ill-posed problem. We also propose instant estimation of a blur kernel arising from the projective transform and the subsequent interpolation of sparse data. Subjective results show significant improvement of image quality. Furthermore, classification of surface type at various distances (evaluated using a support vector machine classifier) is also improved for the images restored using our proposed algorithm.