Digital Image Deblur Based On Wavelet Transform

With the development of science and technology, the requirement for imagequality is higher than before. In particular condition, we cann’t get very clear imagefrom the image acquisition device. There are many factors influencing the imagequality, such as the low quality of image acquisition device. They result in themissing of image detail. Besides, the light intensity may lead to the reduction ofimage contrast. Our purpose is to get high quality image that contains large numberof detais from the degraded image. In practical application, it’s more important toextra detail and edge information. We want to get the license information thatoffends vehicle, or the disease information. Thus, it’s necessary to improve imagequality by restoration of the degraded image.Digital image restoration aims at constructing the original image from thedegraded image. Motion blur is very common in various degraded pattern. Whenthere is relative motion between the image acquisition device and the object, themotion blur occurs. It results in the degrade of image quality. We need to clearimage by programming to get clear image. The thesis analyzes the causes of motionblur, and builds the degraded model. Then the estimation of motion blur parametersis introduced in the model, that contains blur angle and length estimation. At last,Wiener Filter is adopted for restoration. With the appearance of Ringing Effect, Thewhole image deblur process is completed. Analyze the causes of Ringing Effectin the image restoration, we conclude that noise is the main factor. In the process ofobtaining、transporting and saving image, noise is inevitable. Thus, haar wavelet isused for denoising in image pre-processing stage to suppress the ringing effect.The image denoising and deblurring are completed in the thesis, and theconcrete work are as follows:1) Gabor filter is adopted to estimate the blur angle. First,16Gabor filter isconvoluted with the image spectrogram. Then according to the angle of thelargest response, determine the direction of every point. The low-energy part isset to zero. At last, the average angle of non-zero points is taken as the motionblur angle;2) Autocorrelation function is used to calculate the blur length. First, Thedifferential image is computed in the level and then the vertical direction. Thenin the horizontal direction, the autocorrelation function of the differential imageis obtained. Add all the autocorrelation curves, and get the sum autocorrelation curve. According to the distance between the symmetric negative peak of thecurve, the blur length is calculated. Experimental results show that the methodis accurate;3) The Wiener filter is used for image restoration. First, the point spread functionis determined by the blur angle and length. In the frequency domain, Inversefilter is used to restore clear image. Experimental results show that therestoration is very good.4) Haar wavelet is adopted to denoising. Based on the wavelet decomposition andreconstruction principle, we choose haar wavelet to decomposite image. Thenthe high-frequency part is filting. At last reconstruct the image which the noiseis get rid of. Experiments show that in image pre-processing stage, the metodcan suppress the ringing effect created by Wiener filter restoration veryeffectively.The thesis began with blur parameter estimation, which is the key issue. A newmethod for image restoration is proposed that is based on Gabor filter. Waveletdenoising is used to damp the ringing effect. By these processing, the image clarityand quality are improved greatly, and the robustness is enhanced.