We address the multiframe superresolution problem using the variational Bayesian method in this paper. In the variational Bayesian framework, the prior is crucial in transferring the ill-posed reconstruction problem to a well-posed one. We propose a prior combination method based on filter bank and 1 norm. Multiple filters are used in our prior model, and the corresponding combination coefficient vector can be estimated by the characteristics of the filtered image and noise. Furthermore, the local adaptive coefficients of every filter are more effective in removing noise and preserving image edges. Extensive experiments demonstrate the advantages of the proposed method.

In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.INDEX TERMS infrared dim and small target detection, principal component decomposition model, gradient difference regularization principal component decomposition model, overlapping directional multiplier method