subpixel misalignments •Each imposes a set of linear constraints •On the unknown high-resolution intensity values •Enough low-resolution images available Classical Multi-Image
subpixel misalignments •Each imposes a set of linear constraints •On the unknown high-resolution intensity values •Enough low-resolution images available •Set of equations becomes determined & can be solved Classical Multi-Image
subpixel misalignments •Each imposes a set of linear constraints •On the unknown high-resolution intensity values •Enough low-resolution images available •Set of equations becomes determined & can be solved •Limited only to little increase in resolution Classical Multi-Image
subpixel misalignments •Each imposes a set of linear constraints •On the unknown high-resolution intensity values •Enough low-resolution images available •Set of equations becomes determined & can be solved •Limited only to little increase in resolution •By factors smaller than 2 Classical Multi-Image
high resolution images •Learned from database of low-res/high-res image pairs •Usually with a relative scale factor of 2 •Applied to a new low-resolution image Example-based
high resolution images •Learned from database of low-res/high-res image pairs •Usually with a relative scale factor of 2 •Applied to a new low-resolution image •Recover its most likely high-resolution version Example-based
high resolution images •Learned from database of low-res/high-res image pairs •Usually with a relative scale factor of 2 •Applied to a new low-resolution image •Recover its most likely high-resolution version •Exceeds the limits of classical SR Example-based
high resolution images •Learned from database of low-res/high-res image pairs •Usually with a relative scale factor of 2 •Applied to a new low-resolution image •Recover its most likely high-resolution version •Exceeds the limits of classical SR •But not guaranteed to provide the true (unknown) details Example-based
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image •Details beyond the Nyquist frequency
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image •Details beyond the Nyquist frequency •Classical (multi-image)
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image •Details beyond the Nyquist frequency •Classical (multi-image) •High-frequency information are split across multiple low- resolution images (in subpixel aliased form)
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image •Details beyond the Nyquist frequency •Classical (multi-image) •High-frequency information are split across multiple low- resolution images (in subpixel aliased form) •Example-based
•Magnify (up-scale) while maintaining the edge sharpness •Super-Resolution •Recover new missing high-resolution details •Not explicitly found in any individual low-resolution image •Details beyond the Nyquist frequency •Classical (multi-image) •High-frequency information are split across multiple low- resolution images (in subpixel aliased form) •Example-based •High-resolution information are available in patches database, and learned from low-res/high-res pairs
to redundantly recur many times inside the image •within the same scale •Classical SR constraints •and across different scales (coarser) •Provides low-res/high-res pairs for Exampled-based
to redundantly recur many times inside the image •within the same scale •Classical SR constraints •and across different scales (coarser) •Provides low-res/high-res pairs for Exampled-based •Image Completion, Image Re-targeting, Image Denoising
to redundantly recur many times inside the image •within the same scale •Classical SR constraints •and across different scales (coarser) •Provides low-res/high-res pairs for Exampled-based •Image Completion, Image Re-targeting, Image Denoising •Statistically proved later
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is}
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is} •without their DC (average grayscale)
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is} •without their DC (average grayscale) •Distance function: gaussian-weighted SSD
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is} •without their DC (average grayscale) •Distance function: gaussian-weighted SSD •Textured patches have much larger SSD errors
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is} •without their DC (average grayscale) •Distance function: gaussian-weighted SSD •Textured patches have much larger SSD errors •“Good Distance”: SSD with misaligned (0.5px) duplicate
perceive any obvious repetitive structure •vary small patches contain only an edge, a corner, etc •Tested on Berkeley Segmentation Database (BSDS) •Each image I in the database converted to grayscale •Generate down-scaled {Is} from I, as scale factor = 1.25s •s = 0, -1, ..., -6 (I0 = I), smallest one is 1.25-6 = 0.26 of input I •Each patches in I are compared against all patches in {Is} •without their DC (average grayscale) •Distance function: gaussian-weighted SSD •Textured patches have much larger SSD errors •“Good Distance”: SSD with misaligned (0.5px) duplicate •Below “Good Distance” means similar
source patches •with the highest intensity variance •Excludes the uniform and low-frequency patches *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
source patches •with the highest intensity variance •Excludes the uniform and low-frequency patches *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
of low-res images {L1, ..., Ln} given •of the same scene (at subpixel misalignments) •Lj = (H ∗ Bj)↓Sj •↓ denotes a subsampling operation, Sj is subsampling rate
of low-res images {L1, ..., Ln} given •of the same scene (at subpixel misalignments) •Lj = (H ∗ Bj)↓Sj •↓ denotes a subsampling operation, Sj is subsampling rate •Bj(q) is the corresponding Point Spread Function (PSF)
of low-res images {L1, ..., Ln} given •of the same scene (at subpixel misalignments) •Lj = (H ∗ Bj)↓Sj •↓ denotes a subsampling operation, Sj is subsampling rate •Bj(q) is the corresponding Point Spread Function (PSF) •Each low-res pixel p = (x, y) in Lj induces the linear constraint Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q)
of low-res images {L1, ..., Ln} given •of the same scene (at subpixel misalignments) •Lj = (H ∗ Bj)↓Sj •↓ denotes a subsampling operation, Sj is subsampling rate •Bj(q) is the corresponding Point Spread Function (PSF) •Each low-res pixel p = (x, y) in Lj induces the linear constraint Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •on the unknown high-res intensity values {H(Qi} within the neighborhood around its corresponding high-res pixel q ∈ H
of low-res images {L1, ..., Ln} given •of the same scene (at subpixel misalignments) •Lj = (H ∗ Bj)↓Sj •↓ denotes a subsampling operation, Sj is subsampling rate •Bj(q) is the corresponding Point Spread Function (PSF) •Each low-res pixel p = (x, y) in Lj induces the linear constraint Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •on the unknown high-res intensity values {H(Qi} within the neighborhood around its corresponding high-res pixel q ∈ H •size determined by the support of blur kernel B
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5)
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts)
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts) •Treated as if taken from k different low-res images
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts) •Treated as if taken from k different low-res images •of the “same scene”, of course
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts) •Treated as if taken from k different low-res images •of the “same scene”, of course •Each equation induced by Pi is scaled by the degree of similarity of Pi to its patch source P
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts) •Treated as if taken from k different low-res images •of the “same scene”, of course •Each equation induced by Pi is scaled by the degree of similarity of Pi to its patch source P •for increased numerical stability
image L = (H ∗ B)↓S •Recovering H becomes under-determined •constraints induced by L are fewer than the unknowns in H •Let p be a pixel in L, and P be its surrounding patch (5 x 5) •Multiple similar patches P1, ..., Pk in L (at subpixel shifts) •Treated as if taken from k different low-res images •of the “same scene”, of course •Each equation induced by Pi is scaled by the degree of similarity of Pi to its patch source P •for increased numerical stability •patches of high similarity to P have stronger influence
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) •(where s is the scale factor) *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) •(where s is the scale factor) •assuming sufficient neighbors are found *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) •(where s is the scale factor) •assuming sufficient neighbors are found •determined set of linear constraints on the intensity in H *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) •(where s is the scale factor) •assuming sufficient neighbors are found •determined set of linear constraints on the intensity in H •scale each equation by its reliability (patch similarity) *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
pixel in L, find its k nearest patch neighbors in same L •eg. Approximate Nearest Neighbor with k = 9 •compute their subpixel alignment (at 1/s pixel shifts) •(where s is the scale factor) •assuming sufficient neighbors are found •determined set of linear constraints on the intensity in H •scale each equation by its reliability (patch similarity) *NBHF$SFEJU(MBTOFSFUBM l4VQFS3FTPMVUJPOGSPNB4JOHMF*NBHFz
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function •Ij satisfies L = (Ij ∗Bj)↓Sj
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function •Ij satisfies L = (Ij ∗Bj)↓Sj •Although {I0, ..., In} are unknown, the cascade blur kernels {B0, ..., Bn} can be assumed to be known
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function •Ij satisfies L = (Ij ∗Bj)↓Sj •Although {I0, ..., In} are unknown, the cascade blur kernels {B0, ..., Bn} can be assumed to be known •PSF B can be approximated with a gaussian; Bj = B(sj) are a cascade of gaussians (variances determined by sj )
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function •Ij satisfies L = (Ij ∗Bj)↓Sj •Although {I0, ..., In} are unknown, the cascade blur kernels {B0, ..., Bn} can be assumed to be known •PSF B can be approximated with a gaussian; Bj = B(sj) are a cascade of gaussians (variances determined by sj ) •scale factor sj = αj, the constraint Ij = (H ∗ Bn-1)↓Sn-1 still hold for all j
and L = (H ∗ B)↓S •I0, I1, ..., In denote a cascade of unknown images of increasing resolutions (scales) ranging from L to H •I0 = L, In = H, with corresponding blur functions B0, ..., Bn •Bn = B is the PSF relating H to L; B0 is the δ function •Ij satisfies L = (Ij ∗Bj)↓Sj •Although {I0, ..., In} are unknown, the cascade blur kernels {B0, ..., Bn} can be assumed to be known •PSF B can be approximated with a gaussian; Bj = B(sj) are a cascade of gaussians (variances determined by sj ) •scale factor sj = αj, the constraint Ij = (H ∗ Bn-1)↓Sn-1 still hold for all j •uniform scale factor guarantees that Ij and Ij+m are related by the same Bm , regardless of j
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L)
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0 •Let P-j(ṕ) denote such matching patch in I-j , it’s high-res “parent” patch Q0(sj· ṕ) can be extracted from any level between L and I-j
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0 •Let P-j(ṕ) denote such matching patch in I-j , it’s high-res “parent” patch Q0(sj· ṕ) can be extracted from any level between L and I-j •low-res/high-res pair [P, Q]
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0 •Let P-j(ṕ) denote such matching patch in I-j , it’s high-res “parent” patch Q0(sj· ṕ) can be extracted from any level between L and I-j •low-res/high-res pair [P, Q] •high-res parent of low-res P0(p) in L is Qj(sj· ṕ) in unknown Ij ; it can be copied from extracted Q0(sj· ṕ)
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0 •Let P-j(ṕ) denote such matching patch in I-j , it’s high-res “parent” patch Q0(sj· ṕ) can be extracted from any level between L and I-j •low-res/high-res pair [P, Q] •high-res parent of low-res P0(p) in L is Qj(sj· ṕ) in unknown Ij ; it can be copied from extracted Q0(sj· ṕ)
a cascade of unknown images of decreasing resolutions (scales) ranging L, using I-j = (L ∗Bj)↓Sj where j = 0, ..., m •Unlike high-res cascade, low-res {I-j} are known (computed from L) •Let Pj(p) denote a patch in Ij at pixel location p •For any pixel p ∈ L (L = I0 ) and its surround patch P0(p), we search k nearest similar patches within {I-j} where j > 0 •Let P-j(ṕ) denote such matching patch in I-j , it’s high-res “parent” patch Q0(sj· ṕ) can be extracted from any level between L and I-j •low-res/high-res pair [P, Q] •high-res parent of low-res P0(p) in L is Qj(sj· ṕ) in unknown Ij ; it can be copied from extracted Q0(sj· ṕ) P0(p) find NN P-j(ṕ) parent Q0(sj· ṕ) copy Qj(sj· p)
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q)
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •with more compactly supported blur kernel than B = PSF
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •with more compactly supported blur kernel than B = PSF •Solving Coarse-to-Fine
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •with more compactly supported blur kernel than B = PSF •Solving Coarse-to-Fine •use constant factor α = 1.25 (namely, sj = 1.25j)
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •with more compactly supported blur kernel than B = PSF •Solving Coarse-to-Fine •use constant factor α = 1.25 (namely, sj = 1.25j) •when solving equations for image Ij+1
pixel in L •large collection of high-res patch {Qj} between L and H •such “learned” high-res patch Qj induces linear constraints on the unknown target resolution H in the form of Classical SR •Lj(p) = (H ∗ Bj) (q) = ∑qi ∈ Support(Bj) H(qi) Bj(qi - q) •with more compactly supported blur kernel than B = PSF •Solving Coarse-to-Fine •use constant factor α = 1.25 (namely, sj = 1.25j) •when solving equations for image Ij+1 •employ newly recovered high-res images so far (I0, ..., Ij )
and H. C. Andrews, “Cubic Splines for Image Interpolation and Digital Filtering,” IEEE Trans. On Acoustics, Speech, and Signal Processing, 1981 • NEDI: X. Li and M. T. Orchard, “New Edge-Directed Interpolation,” IEEE Trans. On Image Processing, 2001 • GPP: J. Sun, J. Sun, Z. B. Xun and H. Y. Shum, “Image Super-resoluton using Gradient Profile Prior,” CVPR, 2008 • IUES: R. Fattal, “Image upsampling via imposed edge statistics,” Proceeding of ACM SIGGRAPH, 2007 • SRSI: D. Glasner, S. Bagon, M. Irani, “Super-Resolution from a Single Image,” ICCV, 2009
quality •PSNR & MSSIM of bilinear & cubic convolution are larger than new-edge directed interpolation & unified SR •Visual evaluation •primary metric •PSNR & MSSIM don’t provide accurate measure of visual quality in such case
Streaming on Mobile Devices Hong-Han Shuai, De-Nian Yang, Wen-Huang Cheng, Ming-Syan Chen Academia Sinica, National Taiwan University IEEE Transactions on Multimedia (TMM), 2011
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