fractional order derivatives

Fractional-order modeling represents a viable approach for addressing the inherent limitations of total variation in image deblurring tasks.  This technique is achieved through the discretization of fractional derivatives and has demonstrated significant advancements in enhancing the quality of reconstructed images.  Building upon the success of our previous work on blind deconvolution, where we utilized an image-based total variation to reduce the staircase effect, we analyze and test a novel blind deblurring model based on $\beta$-order fractional derivatives using th