PPT-Lecture 2: Image filtering

What is an image A grid matrix of intensity values common to use one byte per value 0 black 255 white 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255. Pre viously we ha depended on fr equencydomain speci64257cations to mak some sort of LP BP HP BS 64257lter which ould xtract the desired information from an input signal No we wish to 64257lter signal to modify it such that it approximates some othe Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?. Algorithms for Image Analysis. Image . Processing Basics. Lecture 3. Lena Gorelick, substituting for Yuri . Boykov. Acknowledgements: slides from Steven Seitz, . Aleosha. . Efros. , David Forsyth, and Gonzalez & Woods. Lecture 20: Image Enhancement in Frequency Domain. Recap of Lecture 19. Spatial filtering. Mean Filter. Non-Local Mean Filter. Median Filter. Unsharp. Masking. Adaptive . Unsharp. Masking. Outline of Lecture 20. CS5670: Intro to Computer Vision. Noah Snavely. Hybrid Images, . Oliva. et al., . http://cvcl.mit.edu/hybridimage.htm. Lecture 1: Images and image filtering. Noah Snavely. Hybrid Images, . Oliva. et al., . Shahar . Kovalsky. Alon. . Faktor. 17/4/2011. IR. Indoor – low light. US. Can we (humans) . denoise. ?. IR. Indoor – low light. US. Sources of Noise. 01010101010101010101010101010101010101010101010101. Computational Photography. Derek Hoiem. 08/31/17. Graphic: . http://www.notcot.org/post/4068/. Administrative stuff. Any questions?. Tutorial. :. Looks like Sept . 6 . at . 5pm (lasting 1.5-2 . hrs. ), . denoising. How can we reduce noise in a photograph?. Let’s replace each pixel with a . weighted. . average. of its neighborhood. The weights are called the . filter kernel. What are the weights for the average of a . Fouhey. Winter 2019, University of Michigan. http://web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/. Note: I’ll ask the front row on the right to participate in a demo. All you have to do is say a number that I’ll give to you. If you don’t want to, it’s fine, but don’t sit in the front. . http://users.cecs.anu.edu.au/~. yili/CVinNutshell.htm. Outline. Paper discussion. Image Processing (overview). Diffusion Process. Image Processing (filtering). Filter Banks. Image Processing (filtering). Fouhey. .. Let’s Take An Image. Let’s Fix Things. Slide Credit: D. Lowe. We have noise in our image. Let’s replace each pixel with a . weighted. average of its neighborhood. Weights are . filter kernel. Minjie. Chen*, . Mantao. . Xu. and . Pasi. . Fränti. Speech and Image Processing Unit (SIPU). School of Computing. University of . Eastern Finland. , . FINLAND. Raster Map Images. Topographic or road maps. 3. Filtering . Filtering image data. is a . standard process . used in almost all image processing systems. . Filters. are used to remove . noise. from digital image while keeping the details of image preserved. . Neighbourhood. Processing. Lecture 2(b). . Neighbourhood. Processing. We have seen . that . an image can be . modified . by applying a particular function to . each pixel value whereby this is known as point processing. .