To improve the visual quality of blur images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. The problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the...

Motion deblurring is a long standing problem in computer vision and image processing. In most previous approaches, the blurred image is modeled as the convolution of a latent intensity image with a blur kernel. However, for images captured by a real camera, the blur convolution should be applied to scene irradiance instead of image intensity and the blurred results need to be mapped back to ima...

An interactive deblurring technique to restore a motion blurred image is proposed in this paper. Segment based semiautomated restoration method is proposed using an error gradient descent iterative algorithm. In this approach, segments are automatically detected which are the best representatives of motion blur. Then the decimal parameters of the blur kernel are interactively derived; with exte...

Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-refere...

We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remaine...

The goal of blind text image deblurring is to obtain a clean from the given blurry without knowing blur kernel. Sparsity-based methods have been shown their effectiveness in various models. However, kernel estimation based on sparse priors lack consideration for brightness information about kernel, which will affect restoration effect Besides, previous seldom apply both spatial domain and trans...

........................................................................................................................................ iii Acknowledgements ...................................................................................................................... iv List of Figures ........................................................................................................

While capturing images of a scene, the relative motion between the camera and the scene during exposure leads to motion-blur in images. The convolution model for motion-blur is applicable only when the camera motion is restricted to in-plane translations. Blur arising due to rotation and out-of-plane translation of camera cannot be modeled using convolution with a single blur kernel [3, 5, 6]. ...

Motion blur is an ill-posed problem which has been addressed for a long time in digital photographing. It usually happens under the circumstance without enough surrounding light. Several methods have been proposed for this problem. However, the ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization-based methods have been proposed, ...

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate m...