In an image restoration process

to obtain good results is challenging because of the unavoidable existence of noise even if the blurring information is already known. To suppress the deterioration caused by noise during the image deblurring process

we propose a new deblurring method with a known kernel. First

the noise in the measurement process is assumed to meet the Gaussian distribution to fit the natural noise distribution. Second

the first and second orders of derivatives are supposed to satisfy the independent Gaussian distribution to control the non-uniform noise. Experimental results show that our method is obviously superior to the Wiener filter

regularized filter

and Richardson-Lucy (RL) algorithm. Moreover

owing to processing in the frequency domain

it runs faster than the other algorithms

in particular about six times faster than the RL algorithm.