No abstract

Denoising is an important issue for laser active image. This paper attempted to process laser active image in the low-dimensional sub-space. We adopted the principal component analysis with local pixel grouping (LPG-PCA) denoising method proposed by Zhang [1], and compared it with the conventional denoising method for laser active image, such as wavelet filtering, wavelet soft threshold filtering and median filtering. Experimental results show that the image denoised by LPG-PCA has higher BIQI value than other images, most of the speckle noise can be reduced and the detail structure information is well preserved. The low-dimensional sub-space idea is a new direction for laser active image denoising.

Modern cameras typically consist up to dozens of individual optical elements to compensate for geometric and chromatic aberrations. In recent year, high-quality single lens imaging has been proved possible by eliminating aberrations with computationally. Point spread function (PSF) calibration is key technique for single lens imaging. However, the existing PSF calibration method is time-consuming and has to be completely repeated for every new lens. This paper proposes a fast PSF calibration to accelerate the process. A PSF model is firstly estimated from a group of single lenses, which is close to the ideal PSF for specific tested single lenses. Then the PSF model is used as the iterative starting point in the calibration optimization. Experimental results show that this method can significantly improve the efficiency of PSF calibration process, and deconvolution performance is competitive with existing methods.

In this paper, we propose a novel two-step seam-searching method based on Dijkstra algorithm in order to improve the seam-searching performance of image mosaic. Parameters of this algorithm are analyzed to improve the overall performance. Experiments show that the proposed method can obtain better seam-searching result than the algorithm based on greedy method and can reduce the computational burden.