“…where the individual trajectory matrices X (i) are computed by (2). The SSA reconstruction of the trajectory matrix X given in the form (3) will then correspond to a simultaneous reconstruction of the image.…”
Section: Ssa For Analyzing Colour Imagesmentioning
confidence: 99%
“…of the same size h × w. To measure the dissimilarity between the two images we shall use the normalized squared Frobenius distance (4) d(I (1) , I (2) )=…”
Section: Denoising the 'Lena' Imagementioning
confidence: 99%
“…Let us fix the size u×v of the moving window and let X (1) , X (2) be two trajectory matrices of size p × q associated with I (1) and I (2) . First, we normalize the trajectory matrices as follows:…”
Section: Defining the Ssa-based Distancesmentioning
confidence: 99%
“…Let us now analyze the two images I (1) and I (2) simultaneously. To do this, we create a joint (normalized) trajectory matrix…”
Section: Figure 10 the Evolution Of The Squared Frobenius Distance For The Reconstruction Of 'Lena' Image Corrupted With Gaussian Noise Omentioning
confidence: 99%
“…If it is needed, from the SVD of the matrix Y Y T we can perform the reconstruction of both images, I (1) and I (2) , using Algorithm 1.…”
Section: Figure 10 the Evolution Of The Squared Frobenius Distance For The Reconstruction Of 'Lena' Image Corrupted With Gaussian Noise Omentioning
A technique of image processing based on application of the Singular Spectrum Analysis (SSA) is discussed and illustrated on the problem of denoising the celebrated 'Lena' image corrupted with noise. Also, SSA-based distances between two images are introduced and suggested for a possible use in the face verification problem.
“…where the individual trajectory matrices X (i) are computed by (2). The SSA reconstruction of the trajectory matrix X given in the form (3) will then correspond to a simultaneous reconstruction of the image.…”
Section: Ssa For Analyzing Colour Imagesmentioning
confidence: 99%
“…of the same size h × w. To measure the dissimilarity between the two images we shall use the normalized squared Frobenius distance (4) d(I (1) , I (2) )=…”
Section: Denoising the 'Lena' Imagementioning
confidence: 99%
“…Let us fix the size u×v of the moving window and let X (1) , X (2) be two trajectory matrices of size p × q associated with I (1) and I (2) . First, we normalize the trajectory matrices as follows:…”
Section: Defining the Ssa-based Distancesmentioning
confidence: 99%
“…Let us now analyze the two images I (1) and I (2) simultaneously. To do this, we create a joint (normalized) trajectory matrix…”
Section: Figure 10 the Evolution Of The Squared Frobenius Distance For The Reconstruction Of 'Lena' Image Corrupted With Gaussian Noise Omentioning
confidence: 99%
“…If it is needed, from the SVD of the matrix Y Y T we can perform the reconstruction of both images, I (1) and I (2) , using Algorithm 1.…”
Section: Figure 10 the Evolution Of The Squared Frobenius Distance For The Reconstruction Of 'Lena' Image Corrupted With Gaussian Noise Omentioning
A technique of image processing based on application of the Singular Spectrum Analysis (SSA) is discussed and illustrated on the problem of denoising the celebrated 'Lena' image corrupted with noise. Also, SSA-based distances between two images are introduced and suggested for a possible use in the face verification problem.
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