Soft-partition-based weighted sum filters for image enhancement

Images are nonstationary in nature. Hard-partition-based weighted sum (HWS) filters give good results in handling both flat regions and details in application such as image enhancement. But due to their non-differentiable filtering operation, their global optimization is difficult to achieve. In this paper, we introduced a new type of soft-partition-based weighted sum (SWS) filters, whose filter operations are differentiable, allowing a steepest decent adaptive optimization to be applied. It is shown that SWS filters provide better performance than HWS filters in an image enhancement application. SWS filters are, therefore, an effective class of nonlinear filters for image enhancement.