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.
Hua-jun FENG Yong-pan WANG Zhi-hai XU Qi LI Hua LEI Ju-feng ZHAO
We propose a new analytical edge spread function （ESF） fitting model to measure the modulation transfer function （MTF）.The ESF data obtained from a slanted-edge image are fitted to our model through the non-linear least squares （NLLSQ） method.The differentiation of the ESF yields the line spread function （LSF）,the Fourier transform of which gives the profile of two-dimensional MTF.Compared with the previous methods,the MTF estimate determined by our method conforms more closely to the reference.A practical application of our MTF measurement in degraded image restoration also validates the accuracy of our model.
