New Methods For Image Deblurring And Denoising: A Statistical Approach

Image restoration has been a long-standing problem in computer vision. Under low-lightconditions, a camera requires either long exposure or a high ISO setting in order to obtain abright image. However, long exposure will result in image motion blur, and high ISO will amplifynoises in the captured image. These two situations can be regarded as image degradation. In thispaper, image restoration methods are proposed to estimate sharp and clean image from acaptured blurry or noisy image.For image motion blur, the properties of linear motion blur in spatial and frequency domainare investigated. The directional high-pass filter is proposed to identify motion direction anddetect motion blurred regions. Specifically, a closed-form solution for motion directionestimation is derived. Regarding general motion blur, this paper studied the gradient statistics ofnatural-scene images and document images. Gradient domain blind deconvolution is proposedand content-aware document image deblurring is presented. Image local constraints areconsidered in order to suppress ringing artifacts. The results produced by the proposed methodsare comparable to that of the state-of-the-art methods.A common assumption in image deblurring is that a blurry image is a linear convolution resultof a sharp image by a Point Spread Function. Nevertheless, in a real camera system, theconvolution is carried out in irradiance domain and Camera Response Function (CRF) willnonlinearly map the convolved irradiance to the output intensity image. This paper presents acomprehensive study on the effects of CRFs on motion deblurring. The approximation errorcaused by the direct intensity convolution is analyzed. We prove that the intensity-basedconvolution closely approximates the irradiance model at low frequency regions, but it will havelarge deviation at high frequency regions. Then, we further propose a CRF estimation methodbased on a pair of sharp/blurred images.Finally, image denoising problem is reviewed. Patch-based denoising, like Non-Local Means(NLM) and BM3D, is able to produce high-quality result. However, they require expensivepair-wise patch comparisons. This paper introduces a Patch Geodesic (PG) distance metric forpatch comparison. In order to reduce noise at multiple scales, we adopt Laplacian-Gaussianimage decomposition and apply PG-based denoising at each scale. It achieves comparable qualityas the state-of-the-art methods. Since PG path can be efficiently approximated by minimum hoppath, the proposed method is a few orders of magnitude faster than traditional patch-baseddenoising methods without losing quality.