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Filter and removal artifact (Lọc nhiễu trên ảnh)
Trường Cao Đẳng Nguyễn Tất Thành Ngành kỹ thuật y sinh Filter and removal artifact Mục tiêu học Biết số loại nhiễu hình ảnh Hiểu phép biến đổi Fourier chiều ứng dụng xử lý ảnh Biết số phương pháp lọc ảnh Ứng dụng Matlab xử lý ảnh Content Noise Fourier transformer DFT Applications Matlab simulator Noise Random noise Random noise la nhiễu xuất trình ngẫu nhiên nhiễu nhiệt thiết bị điện tử đếm photon Để giảm nhiễu ta cần làm lạnh cho thiết bị điện tử,đặc biệt bán dẫn Structured noise Dạng phổ biến nhiễu cấu trúc ảnh y sinh nhiễu tia X bị tán xạ dùng lưới chống tán xạ (grid antiscatter) để loại bỏ Physiological interference Do bệnh nhân thở chụp ngực tia X Do nhu động ruột,thở CT bụng Do hoạt động tim CT ngực Sự dịch chuyển động mạch ảnh xóa Other types of noise and artifact Những điểm lốm đốm bụi màn,film bàn Sự trầy xước phim Nhiễu điểm detector Nhiễu muối tiêu pixel Nhiễu quần áo,đồ trang sức… Nhiễu mỹ phẩm chất khử mùi Noise Noise K - Space Discrete Fourier Transform (DFT) Noise – frequency domain filtering Noise – frequency domain filtering Fourier transformer Ideal Low Pass Filter The transfer function for the ideal low pass filter can be given as: 1 if D(u , v) ≤ D0 H (u , v) = 0 if D (u, v) > D0 where D(u,v) is given as: D (u , v) = [(u − M / 2) + (v − N / 2) ] 2 1/ Ideal Low Pass Filter Original image Result of filtering with ideal low pass filter of radius Result of filtering with ideal low pass filter of radius 15 Result of filtering with ideal low pass filter of radius 30 Result of filtering with ideal low pass filter of radius 80 Result of filtering with ideal low pass filter of radius 230 Ideal High Pass Filters The ideal high pass filter is given as: 0 if D(u , v) ≤ D0 H (u , v) = 1 if D (u , v) > D0 where D0 is the cut off distance as before Ideal High Pass Filters Results of ideal high pass filtering with D0 = 15 Results of ideal high pass filtering with D0 = 30 Results of ideal high pass filtering with D0 = 80 Original image Highpass filtering result After histogram equalisation High frequency emphasis result Highpass Filtering Example Fourier space filtering Low Pass Filter High Pass Filter How frequencies show up in an image? Low frequencies correspond to slowly varying information (e.g., continuous surface) High frequencies correspond to quickly varying information (e.g., edges) Original Image Low-passed Matlab simulator Help “Work” folder m-file % Một số lệnh Matlab imread imview, imshow, imadd imsubtract fft2, ifft2, imhist , histeq radon fanbeam improfile dicomread Documents Digital signal processing using matlab and wavelets Biosignal and biomedical image processing [...]... 2 Ideal Low Pass Filter Original image Result of filtering with ideal low pass filter of radius 5 Result of filtering with ideal low pass filter of radius 15 Result of filtering with ideal low pass filter of radius 30 Result of filtering with ideal low pass filter of radius 80 Result of filtering with ideal low pass filter of radius 230 Ideal High Pass Filters The ideal high pass filter is given as:... remove high frequencies frequencies reconstructed signal Time Domain and Frequency Domain Fourier space filtering Fourier space filtering K - Space Discrete Fourier Transform (DFT) Noise – frequency domain filtering Noise – frequency domain filtering Fourier transformer Ideal Low Pass Filter The transfer function for the ideal low pass filter can be given as: 1 if D(u , v) ≤ D0 H (u , v) = 0 if D... v) > D0 where D0 is the cut off distance as before Ideal High Pass Filters Results of ideal high pass filtering with D0 = 15 Results of ideal high pass filtering with D0 = 30 Results of ideal high pass filtering with D0 = 80 Original image Highpass filtering result After histogram equalisation High frequency emphasis result Highpass Filtering Example ... second-order term … Fourier Series Time Domain and Frequency Domain Time Domain: Tells us how properties (air pressure in a sound function, for example) change over time: Amplitude = 100 Frequency = number of cycles in one second = 200 Hz Time Domain and Frequency Domain Frequency domain: Tells us how properties (amplitudes) change over frequencies: Time Domain and Frequency Domain Fourier transformer