PDF-59 SAR Image Filtering in Wavelet Domain by Subband Depend

TNabil 62. 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. Signal Analysis. 09 . Oct 2015. © A.R. Lowry . 2015. Last time. :. • . A . Periodogram. . is the squared modulus of the signal FFT. !. • . Blackman-. Tukey. estimates autocorrelation from signal, then. Image . Denoising. 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. Modeling of . Graph-Structured. Data . … and … . Images. 1. Michael Elad. The Computer Science Department . The Technion . T. he . research leading . to these results . (Section 13.10.6-13.10.8). Michael Phipps. Vallary. S. . Bhopatkar. The most useful thing about wavelet transform is that it can turned into sparse expansion i.e. it can be truncated. Truncated Wavelet Approximation. Federica Caselli. . Department of Civil Engineering University . of Rome Tor . Vergata. Corso. . di. . Modellazione. e . Simulazione. . di. . Sistemi. . Fisiologici. Medical Imaging. X-Ray. CT. 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., . Lecture . 5. DCT & Wavelets. Tammy . Riklin. Raviv. Electrical and Computer Engineering. Ben-Gurion University of the Negev. Spatial Frequency Analysis. images of naturally occurring scenes or objects (trees, rocks, . 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. Michael Phipps. Vallary. S. . Bhopatkar. The most useful thing about wavelet transform is that it can turned into sparse expansion i.e. it can be truncated. Truncated Wavelet Approximation. Arbitrary chosen . S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. Overview. 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. 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. 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. .