tailieunhanh - Digital Image Processing: Image Restoration - Duong Anh Duc

Digital Image Processing: Image Restoration - Duong Anh Duc includes Image Restoration; Restoration vs. Enhancement; Degradation Model; Gaussian noise; Erlang(Gama) noise; Exponential noise; Impulse (salt-and-pepper) noise; Plot of density function of different noise models. | 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Digital Image Processing Image Restoration 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Image Restoration Most images obtained by optical, electronic, or electro-optic means is likely to be degraded. The degradation can be due to camera misfocus, relative motion between camera and object, noise in electronic sensors, atmospheric turbulence, etc. The goal of image restoration is to obtain a relatively “clean” image from the degraded observation. It involves techniques like filtering, noise reduction etc. 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Restoration vs. Enhancement Restoration: A process that attempts to reconstruct or recover an image that has been degraded by using some prior knowledge of the degradation phenomenon. Involves modeling the degradation process and applying the inverse process to recover the original image. A criterion for “goodness” is required that . | 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing Digital Image Processing Image Restoration 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing Image Restoration Most images obtained by optical, electronic, or electro-optic means is likely to be degraded. The degradation can be due to camera misfocus, relative motion between camera and object, noise in electronic sensors, atmospheric turbulence, etc. The goal of image restoration is to obtain a relatively “clean” image from the degraded observation. It involves techniques like filtering, noise reduction etc. 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing Restoration vs. Enhancement Restoration: A process that attempts to reconstruct or recover an image that has been degraded by using some prior knowledge of the degradation phenomenon. Involves modeling the degradation process and applying the inverse process to recover the original image. A criterion for “goodness” is required that will recover the image in an optimal fashion with respect to that criterion. Ex. Removal of blur by applying a deblurring function. 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing Restoration vs. Enhancement Enhancement: Manipulating an image in order to take advantage of the psychophysics of the human visual system. Techniques are usually “heuristic.” Ex. Contrast stretching, histogram equalization. 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing (Linear) Degradation Model g(m,n) = f(m,n)*h(m,n) + (m,n) G(u,v) = H(u,v)F(u,v) + N(u,v) f(m,n) : Degradation free image g(m,n) : Observed image h(m,n) : PSS of blur degradation (m,n) : Additive Noise 5/14/2020 5:31:15 AM Duong Anh Duc - Digital Image Processing (Linear) Degradation Model We need to find an image ^f (m,n) , such that the error f (m,n) - ^f (m,n) is “small.” Problem: Given an observed image g(m,n) , to recover the original image f(m,n) , using knowledge about the blur function h(m,n) and