TAILIEUCHUNG - Digital Image Processing: Image Restoration Matrix Formulation - Duong Anh Duc

Digital Image Processing: Image Restoration Matrix Formulation - Duong Anh Duc provides about matrix Formulation of Image Restoration Problem; constrained least squares filtering (restoration); a brief review of matrix differentiation; Pseudo-inverse Filtering; Minimum Mean Square Error (Wiener) Filter; Parametric Wiener Filter. | 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Digital Image Processing Image Restoration Matrix Formulation 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Matrix Formulation of Image Restoration Problem 1-D Case: We will consider the 1-D version first, for simplicity: g(m) = f(m)*h(m) + (m) We will assume that the arrays f and h have been zero-padded to be of size M, where M length(f) + length(h) - 1. Henceforth, we will not explicitly mention the zero-padding. The degradation equation: can be written in matrix-vector form as follows: g = Hf + n, where 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Matrix Formulation of Image Restoration Problem 5/14/2020 4:35:04 AM Duong Anh Duc - Digital Image Processing Matrix Formulation of Image Restoration Problem However, since the arrays f and h are zero-padded, we can equivalently set: Notice that the (second) matrix H is circulant; ., each row of H is a circular shift of

• Đồ họa - Thiết kế - Flash
• Digital Image Processing
• Image Restoration Matrix Formulation
• Constrained least squares filtering
• Brief review of matrix differentiation
• Pseudo inverse Filtering
• Parametric Wiener Filter
• Image segmentation
• Lecture Digital image processing
• Digital images
• Digital computers
• Image processing
• Local processing
• Global processing
• Morphological image processing
• Autonomous machine applications
• Region growing
• Image digitization
• Theory of sampling
• Image transformation
• Image enhancement
• Image restoration
• Image transform
• Image compression
• Dark image