Research On Motion Deblurring Technologies For Image Quality Enhancement

In photography, camera shaking or object relative movement may cause final images or videos blurry. In order to overcome the quality loss in blurry effect, image deblurring techniques have been widely studied in computer vision and digital image processing in recent years. Generally, image blurring can be classified into two basic categories: defocus blurring and motion blurring. When the position of object in image is out of focus or far from the optical focal length, result photos will have defocus blurring. Sometimes, depth of field is one of the photography techniques. In such case, people intentionally make the background out of focus. Motion blurring is the movement effect of object in scene. The main reason for motion blurring is the relative motion between lens and scene. The lens accumulates incoming light during the motion. This paper mainly researches on motion deblurring.Mathematically, motion blurring is considered as a convolution process. The blurry image is made up by random noise and the convolution result of latent image and a blur kernel. The blur kernel is also called point-spread-function (PSF). It describes essential information about light accumulation in a small region, for example, light intensity density and position. According to the accessible of the blur kernel, there are two kinds of motion deblurring: blind deconvolution and non-blind deconvolution. According to the unicity of blur kernel, motion deblurring can be classified into motion variant deblur (multiple kernels) and motion invariant deblur (single kernel). In order to restore latent clear image as accurate as possible, there are many challenges in motion deblurring. (1) Unknown blur kernel. Single image based deblurring need to use several methods to accurately estimate blur kernel. However, the essential estimation information is loss in the blurry image. (2) Multiple solution of deconvolution. Because the solution of a deconvolution is no unique, result image may be still fuzzy. (3) Ringing effect. This is caused by the deconvolution model itself, for example finite Fourier series. (4) Noise. Normal deblurring method is sensitive to noise. This makes image restoration difficult.This paper focus on single image based motion deblurring. It developed research on motion deblurring on image quality enhancement from three aspects. First, we try to estimate blur kernel using other techniques such as gradient prior, image filter and gradient domain method. We can estimate the size, position and density of the kernel. Second, we develop a robust deconvolution model, which is stable and not sensitive to noise. It can overcome the ringing effect in previous deblur method. Third, we try to tackle motion variant deblurring, which consider multiple blur kernel and can get more accurate result.This paper proposed a novel edge-preserving gradient enhancement is used in kernel estimation, which can recover sharp edges in blurry image without intensifying the derivatives or noise in flat area. Experiment results show that our method can not only estimate blur kernel accurately but also restore clear image. This deblurring technique will have good application prospects in digital photography.