基于区域选择的快速POCS超分辨率复原算法研究

Research on fast POCS super- resolution restoration algorithm based on region selection

POCS算法是目前超分辨率复原中应用非常广泛的一种复原算法,但是该算法运算量大,处理时间较长。针对POCS算法迭代时间较长、无法满足实时性的问题,提出了基于区域选择的快速POCS超分辨率复原算法(TPOCS)。光电探测系统中关注的重点是目标区域,而这一区域通常只占很少的像素位置,因此通过阈值分割和合并找到所有目标区域并集,然后仅在这个目标区域并集上进行超分辨率复原。实验结果表明:TPOCS算法去除了复原背景的巨大运算量,大大缩短了运算时间,减少了2个数量级使其达到实时,整体复原效果优于POCS算法。TPOCS算法能够自适应的选择目标区域,在保证复原性能的基础上大大缩短了运算时间,使其达到实时,进而可以在红外图像处理系统中应用。

POCS algorithm is a restoration algorithm which is widely used in super-resolution restoration. But this algorithm has large amount of computation and takes a long treatment time. Simultaneously, the details on the edge of the image are poor retention capacity. For the long iteration of the POCS super-resolution restoration algorithm and the shortcomings of incapability to meet the real-time detecting of optical detection system, a fast POCS super- resolution restoration algorithm based on the region selection （TPOCS） is proposed. The target area is the key point we focus on in the optical detection system, while this area contains only very small number of pixels. Therefore, we use threshold segmentation and combination to acquire the union of all target areas. Then we execute super-reso- lution restoration only in the union of all target areas. The experimental results show that TPOCS algorithm can de- crease the huge computation of background restoration and greatly reduce the operation time to achieve real-time. The overall resilience of the restoration method is superior to the traditional POCS. TPOCS algorithm could adap- tively select the target region and decrease the huge computation of background restoration. Furthermore, TPOCS algorithm can guarantee the performance of super-resolution restoration on the basis of greatly reducing the process- ing time to achieve real-time. So this super-resolution restoration algorithm can be applied in the practical infrared image processing system.