From 03eb63b677489da279cf82e5f50ffc6ee308d00c Mon Sep 17 00:00:00 2001
From: WolframRhodium <WolframRhodium@users.noreply.github.com>
Date: Fri, 21 Dec 2018 11:23:06 +0800
Subject: [PATCH] Add linear methods, e.g. bicubic

2. Update citations
---
 CARN/progressive/README.md                    |   4 +-
 DBPN/README.md                                |   4 +-
 IDN/README.md                                 |   2 +-
 ProSR/README.md                               |   4 +-
 .../README.md                                 |   2 +-
 SESR/README.md                                |   2 +-
 TSCN/README.md                                |   2 +-
 WDSR/wdsr@krasserm/README.md                  |   2 +-
 linear_method/bicubic_2x-0000.params          | Bin 0 -> 270 bytes
 linear_method/bicubic_2x-symbol.json          |  83 +++++++++++++
 linear_method/example_produce_resizer.py      |  71 +++++++++++
 linear_method/resize_weight.py                | 114 ++++++++++++++++++
 xUnit/README.md                               |   2 +-
 13 files changed, 280 insertions(+), 12 deletions(-)
 create mode 100644 linear_method/bicubic_2x-0000.params
 create mode 100644 linear_method/bicubic_2x-symbol.json
 create mode 100644 linear_method/example_produce_resizer.py
 create mode 100644 linear_method/resize_weight.py

diff --git a/CARN/progressive/README.md b/CARN/progressive/README.md
index ce9b576..ac1416b 100644
--- a/CARN/progressive/README.md
+++ b/CARN/progressive/README.md
@@ -3,6 +3,6 @@ Converted from **official** [nmhkahn/NTIRE2018](https://github.com/nmhkahn/NTIRE
 ---
 
 ### Reference
-**[1] Ahn, N., Kang, B., & Sohn, K. A. (2018). Image Super-resolution via Progressive Cascading Residual Network. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Ahn_Image_Super-Resolution_via_CVPR_2018_paper.pdf) [[Code]](https://github.com/nmhkahn/NTIRE2018)**
+**[1] Ahn, N., Kang, B., & Sohn, K. A. (2018). [Image Super-resolution via Progressive Cascading Residual Network](https://ieeexplore.ieee.org/document/8575275). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Ahn_Image_Super-Resolution_via_CVPR_2018_paper.pdf) [[Code]](https://github.com/nmhkahn/NTIRE2018)**
 
-[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results. In proceedings of the IEEE Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
+[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://ieeexplore.ieee.org/document/8575282/). In the IEEE/CVF Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
diff --git a/DBPN/README.md b/DBPN/README.md
index afd3960..4ada30b 100644
--- a/DBPN/README.md
+++ b/DBPN/README.md
@@ -3,6 +3,6 @@ Converted from **official** [alterzero/DBPN-caffe](https://github.com/alterzero/
 ---
 
 ### Reference
-**[1] Haris, M., Shakhnarovich, G., & Ukita, N. (2018). Deep Back-Projection Networks For Super-Resolution. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [[PDF]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN_cvpr2018_final.pdf) [[Code-Caffe]](https://github.com/alterzero/DBPN-caffe) [[Code-PyTorch]](https://github.com/alterzero/DBPN-Pytorch) [[Supplementary Material]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN_cvpr2018_supp_material.pdf) [[Project]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN.html)**
+**[1] Haris, M., Shakhnarovich, G., & Ukita, N. (2018). [Deep Back-Projection Networks For Super-Resolution](https://ieeexplore.ieee.org/document/8578277/). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[PDF]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN_cvpr2018_final.pdf) [[Code-Caffe]](https://github.com/alterzero/DBPN-caffe) [[Code-PyTorch]](https://github.com/alterzero/DBPN-Pytorch) [[Supplementary Material]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN_cvpr2018_supp_material.pdf) [[Project]](https://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN.html)**
 
-[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results. In proceedings of the IEEE Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
+[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://ieeexplore.ieee.org/document/8575282/). In the IEEE/CVF Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
diff --git a/IDN/README.md b/IDN/README.md
index a27689c..b09c8d0 100644
--- a/IDN/README.md
+++ b/IDN/README.md
@@ -3,4 +3,4 @@ Converted from **official** [Zheng222/IDN-Caffe](https://github.com/Zheng222/IDN
 ---
 
 ### Reference
-[1] Hui, Z., Wang, X. & Gao, X. (2018). Fast and Accurate Single Image Super-Resolution via Information Distillation Network. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_Fast_and_Accurate_CVPR_2018_paper.pdf) [[Code]](https://github.com/Zheng222/IDN-Caffe) [[Poster]](https://github.com/Zheng222/IDN-Caffe/blob/2bcc23e4f66684cb7e1fbb3ceb4a957f830b83e6/files/cvpr18_poster.pdf)
+[1] Hui, Z., Wang, X. & Gao, X. (2018). [Fast and Accurate Single Image Super-Resolution via Information Distillation Network](https://ieeexplore.ieee.org/document/8578180/). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_Fast_and_Accurate_CVPR_2018_paper.pdf) [[Code]](https://github.com/Zheng222/IDN-Caffe) [[Poster]](https://github.com/Zheng222/IDN-Caffe/blob/2bcc23e4f66684cb7e1fbb3ceb4a957f830b83e6/files/cvpr18_poster.pdf)
diff --git a/ProSR/README.md b/ProSR/README.md
index 21f1c74..07624e9 100644
--- a/ProSR/README.md
+++ b/ProSR/README.md
@@ -3,6 +3,6 @@ Converted from **official** [fperazzi/proSR](https://github.com/fperazzi/proSR/t
 ---
 
 ### Reference
-**[1] Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., & Schroers, C. (2018). A Fully Progressive Approach to Single-Image Super-Resolution. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://igl.ethz.ch/projects/prosr/prosr-cvprw-2018-wang-et-al.pdf) [[Code]](https://github.com/fperazzi/proSR) [[Supplementary Material]](http://igl.ethz.ch/projects/prosr/prosr-supplemental.pdf) [[Video]](http://igl.ethz.ch/projects/prosr/prosr-video.mp4) [[Project]](http://igl.ethz.ch/projects/prosr/)**
+**[1] Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., & Schroers, C. (2018). [A Fully Progressive Approach to Single-Image Super-Resolution](https://ieeexplore.ieee.org/document/8575284/). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://igl.ethz.ch/projects/prosr/prosr-cvprw-2018-wang-et-al.pdf) [[Code]](https://github.com/fperazzi/proSR) [[Supplementary Material]](http://igl.ethz.ch/projects/prosr/prosr-supplemental.pdf) [[Video]](http://igl.ethz.ch/projects/prosr/prosr-video.mp4) [[Project]](http://igl.ethz.ch/projects/prosr/)**
 
-[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results. In proceedings of the IEEE Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
+[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://ieeexplore.ieee.org/document/8575282/). In the IEEE/CVF Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
diff --git a/RDN/Residual-Dense-Network-Caffe@xueshengke/README.md b/RDN/Residual-Dense-Network-Caffe@xueshengke/README.md
index d723a48..9c66907 100644
--- a/RDN/Residual-Dense-Network-Caffe@xueshengke/README.md
+++ b/RDN/Residual-Dense-Network-Caffe@xueshengke/README.md
@@ -3,4 +3,4 @@ Converted from [xueshengke/Residual-Dense-Network-Caffe](https://github.com/xues
 ---
 
 ### Reference
-[1] Zhang, Y., Tian, Y., Kong, Y., Zhong, B. & Fu, Y. (2018). Residual Dense Network for Image Super-Resolution. In the IEEE Conference on Computer Vision and Pattern Recognition. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf) [[Code]](https://github.com/yulunzhang/RDN)
+[1] Zhang, Y., Tian, Y., Kong, Y., Zhong, B. & Fu, Y. (2018). [Residual Dense Network for Image Super-Resolution](https://ieeexplore.ieee.org/document/8578360/). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[PDF]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf) [[Code]](https://github.com/yulunzhang/RDN)
diff --git a/SESR/README.md b/SESR/README.md
index 7a90f20..ae5880d 100644
--- a/SESR/README.md
+++ b/SESR/README.md
@@ -3,4 +3,4 @@ Converted from **official** [opteroncx/SESR](https://github.com/opteroncx/SESR/t
 ---
 
 ### Reference
-[1] Cheng, X., Li, X., Yang, J. & Tai, Y. (2018). SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks. In proceedings of International Conference on Pattern Recognition. [[PDF]](https://arxiv.org/pdf/1801.10319) [[Code]](https://github.com/opteroncx/SESR)
+[1] Cheng, X., Li, X., Yang, J. & Tai, Y. (2018). [SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks](https://ieeexplore.ieee.org/document/8546130/). International Conference on Pattern Recognition. [[PDF]](https://arxiv.org/pdf/1801.10319) [[Code]](https://github.com/opteroncx/SESR)
diff --git a/TSCN/README.md b/TSCN/README.md
index 5d953a9..96d7509 100644
--- a/TSCN/README.md
+++ b/TSCN/README.md
@@ -3,4 +3,4 @@ Converted from **official** [Zheng222/TSCN](https://github.com/Zheng222/TSCN/com
 ---
 
 ### Reference
-[1] Hui, Z., Wang, X., & Gao, X. (2018). Two-Stage Convolutional Network for Image Super-Resolution. In proceedings of International Conference on Pattern Recognition. [[PDF]](https://github.com/Zheng222/TSCN/blob/20818c148a6f02a0d5e7c75553528f52dbebcf6a/files/TSCN.pdf) [[Code]](https://github.com/Zheng222/TSCN) [[Poster]](https://github.com/Zheng222/TSCN/raw/master/files/icpr18_poster.pdf)
\ No newline at end of file
+[1] Hui, Z., Wang, X., & Gao, X. (2018). [Two-Stage Convolutional Network for Image Super-Resolution](https://ieeexplore.ieee.org/document/8545648/). International Conference on Pattern Recognition. [[PDF]](https://github.com/Zheng222/TSCN/blob/20818c148a6f02a0d5e7c75553528f52dbebcf6a/files/TSCN.pdf) [[Code]](https://github.com/Zheng222/TSCN) [[Poster]](https://github.com/Zheng222/TSCN/raw/master/files/icpr18_poster.pdf)
\ No newline at end of file
diff --git a/WDSR/wdsr@krasserm/README.md b/WDSR/wdsr@krasserm/README.md
index a5f9697..eeec25a 100644
--- a/WDSR/wdsr@krasserm/README.md
+++ b/WDSR/wdsr@krasserm/README.md
@@ -5,4 +5,4 @@ Converted from [krasserm/wdsr](https://github.com/krasserm/wdsr/tree/4701c31b8fb
 ### Reference
 **[1] Yu, J. H., Fan, Y. C., Yang, J. C., Xu, N., Wang, Z. W., Wang, X. C. & Huang, T.  (2018). Wide Activation for Efficient and Accurate Image Super-Resolution. arXiv preprint arXiv:1808.08718. [[PDF]](https://arxiv.org/pdf/1808.08718) [[Code]](https://github.com/JiahuiYu/wdsr_ntire2018)**
 
-[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results. In proceedings of the IEEE Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
+[2] Timofte, R., Gu, S. H., Wu, J. Q., Van Gool, L., Zhang, L., Yang, M. H., ... & Fu, L. Z. (2018). [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://ieeexplore.ieee.org/document/8575282/). In the IEEE/CVF Converence on Computer Vision and Pattern Recognition Workshops. [[PDF]](http://people.ee.ethz.ch/~timofter/publications/NTIRE2018_SR_report_CVPRW-2018.pdf) [[Project]](http://www.vision.ee.ethz.ch/ntire18/#challenge)
diff --git a/linear_method/bicubic_2x-0000.params b/linear_method/bicubic_2x-0000.params
new file mode 100644
index 0000000000000000000000000000000000000000..1d0b01c19a8e37ff53b7b78dc4c44e0229b1e4ee
GIT binary patch
literal 270
zcmWe)WIzE-P~OR3lYfGkEI<rX0;4&gYG8aA4G})Dhk;?jUIvB?I|hb0dk6-xL1G8?
u;4>Fy4!Zfom<w~aIMi*4Md?;anaQO<9B)(+pAlc4nwg$af<w9-Dh&XD=_xw^

literal 0
HcmV?d00001

diff --git a/linear_method/bicubic_2x-symbol.json b/linear_method/bicubic_2x-symbol.json
new file mode 100644
index 0000000..4eae7e9
--- /dev/null
+++ b/linear_method/bicubic_2x-symbol.json
@@ -0,0 +1,83 @@
+{
+  "nodes": [
+    {
+      "op": "null", 
+      "name": "data", 
+      "inputs": []
+    }, 
+    {
+      "op": "Pad", 
+      "name": "pad0", 
+      "attrs": {
+        "mode": "edge", 
+        "pad_width": "(0, 0, 0, 0, 2, 2, 0, 0)"
+      }, 
+      "inputs": [[0, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "bicubic_2x_h_weight", 
+      "attrs": {
+        "kernel": "(8, 1)", 
+        "no_bias": "True", 
+        "num_filter": "1", 
+        "num_group": "1", 
+        "pad": "(7, 0)", 
+        "stride": "(2, 1)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "Deconvolution", 
+      "name": "bicubic_2x_h", 
+      "attrs": {
+        "kernel": "(8, 1)", 
+        "no_bias": "True", 
+        "num_filter": "1", 
+        "num_group": "1", 
+        "pad": "(7, 0)", 
+        "stride": "(2, 1)"
+      }, 
+      "inputs": [[1, 0, 0], [2, 0, 0]]
+    }, 
+    {
+      "op": "Pad", 
+      "name": "pad1", 
+      "attrs": {
+        "mode": "edge", 
+        "pad_width": "(0, 0, 0, 0, 0, 0, 2, 2)"
+      }, 
+      "inputs": [[3, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "bicubic_2x_w_weight", 
+      "attrs": {
+        "kernel": "(1, 8)", 
+        "no_bias": "True", 
+        "num_filter": "1", 
+        "num_group": "1", 
+        "pad": "(0, 7)", 
+        "stride": "(1, 2)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "Deconvolution", 
+      "name": "bicubic_2x_w", 
+      "attrs": {
+        "kernel": "(1, 8)", 
+        "no_bias": "True", 
+        "num_filter": "1", 
+        "num_group": "1", 
+        "pad": "(0, 7)", 
+        "stride": "(1, 2)"
+      }, 
+      "inputs": [[4, 0, 0], [5, 0, 0]]
+    }
+  ], 
+  "arg_nodes": [0, 2, 5], 
+  "node_row_ptr": [0, 1, 2, 3, 4, 5, 6, 7], 
+  "heads": [[6, 0, 0]], 
+  "attrs": {"mxnet_version": ["int", 10300]}
+}
\ No newline at end of file
diff --git a/linear_method/example_produce_resizer.py b/linear_method/example_produce_resizer.py
new file mode 100644
index 0000000..7e4f147
--- /dev/null
+++ b/linear_method/example_produce_resizer.py
@@ -0,0 +1,71 @@
+import mxnet as mx
+from resize_weight import resize_weight
+
+# a script that produces a (center-aligned) linear interpolation resizer in the form of CNN in MXNet
+
+# params
+scale = 2
+kernel = 'bicubic'
+channels = 1
+downsample = False
+
+if not downsample:
+    name = '{}_{}x'.format(kernel, scale)
+
+    weight_1d = resize_weight(scale=scale, kernel=kernel)
+    width = len(weight_1d)
+    taps = (width + 1) // (2 * scale)
+
+    # tries to fix the boundary condition via external padding, but the value here might not be accurate
+    pad = scale // 2 + (2 * taps - 1) * scale
+
+    # Defining the network
+    # Since the interpolations are separable, we implemented them via spatial separable convolutions
+    # It's straightforward to modify the code to obtain a single convolution implementation
+    data = mx.symbol.Variable('data') # assumes NCHW data format
+    upsample_h = mx.symbol.pad(data, mode='edge', pad_width=(0, 0, 0, 0, taps, taps, 0, 0))
+    upsample_h = mx.symbol.Deconvolution(upsample_h, kernel=(width, 1), stride=(scale, 1), pad=(pad, 0), 
+        num_filter=channels, num_group=channels, no_bias=True, name='{}_h'.format(name))
+    upsample_w = mx.symbol.pad(upsample_h, mode='edge', pad_width=(0, 0, 0, 0, 0, 0, taps, taps))
+    upsample_w = mx.symbol.Deconvolution(upsample_w, kernel=(1, width), stride=(1, scale), pad=(0, pad), 
+        num_filter=channels, num_group=channels, no_bias=True, name='{}_w'.format(name))
+
+    # Loading weights
+    net = mx.gluon.SymbolBlock(outputs=upsample_w, inputs=data)
+    net_params = net.collect_params()
+    net_params['{}_h_weight'.format(name)]._load_init(mx.nd.array(weight_1d.reshape((1, 1, width, 1))), ctx=mx.cpu())
+    net_params['{}_w_weight'.format(name)]._load_init(mx.nd.array(weight_1d.reshape((1, 1, 1, width))), ctx=mx.cpu())
+    net.hybridize()
+    test_data = mx.nd.ones((1, channels, 96, 96))
+    net.forward(test_data)
+
+    # Output
+    net.export(name)
+
+else:
+    name = '{}_{}x_downsample'.format(kernel, scale)
+
+    weight_1d = resize_weight(scale=scale, kernel=kernel, downsample=True)
+    width = len(weight_1d)
+    taps = (width + 1) // (2 * scale)
+    pad = scale // 2 + (2 * taps - 1) * scale
+
+    data = mx.symbol.Variable('data')
+    upsample_h = mx.symbol.pad(data, mode='edge', pad_width=(0, 0, 0, 0, 2 * taps - 1, 2 * taps, 0, 0))
+    upsample_h = mx.symbol.Deconvolution(upsample_h, kernel=(width, 1), stride=(scale, 1), pad=(pad, 0), 
+        num_filter=channels, num_group=channels, no_bias=True, name='{}_h'.format(name))
+    upsample_w = mx.symbol.pad(upsample_h, mode='edge', pad_width=(0, 0, 0, 0, 0, 0, 2 * taps - 1, 2 * taps - 1))
+    upsample_w = mx.symbol.Deconvolution(upsample_w, kernel=(1, width), stride=(1, scale), pad=(0, pad), 
+        num_filter=channels, num_group=channels, no_bias=True, name='{}_w'.format(name))
+
+    net = mx.gluon.SymbolBlock(outputs=upsample_w, inputs=data)
+    net_params = net.collect_params()
+    net_params['{}_h_weight'.format(name)]._load_init(mx.nd.array(weight_1d.reshape((1, 1, width, 1))), ctx=mx.cpu())
+    net_params['{}_w_weight'.format(name)]._load_init(mx.nd.array(weight_1d.reshape((1, 1, 1, width))), ctx=mx.cpu())
+
+    net.hybridize()
+
+    test_data = mx.nd.ones((1, channels, 96, 96))
+    net.forward(test_data)
+
+    net.export(name)
\ No newline at end of file
diff --git a/linear_method/resize_weight.py b/linear_method/resize_weight.py
new file mode 100644
index 0000000..511215a
--- /dev/null
+++ b/linear_method/resize_weight.py
@@ -0,0 +1,114 @@
+import numpy as np
+import functools
+
+_vectorize_fp64 = functools.partial(np.vectorize, otypes=[np.float64])
+
+@_vectorize_fp64
+def box(x):
+    """matlab.images.internal.resize.box"""
+
+    return -0.5 <= x < 0.5
+
+@_vectorize_fp64
+def triangle(x):
+    """matlab.images.internal.resize.triangle"""
+
+    return (x + 1) * (-1 <= x < 0) + (1 - x) * (0 <= x <= 1)
+
+@_vectorize_fp64
+def cubic(x):
+    """matlab.images.internal.resize.cubic"""
+
+    absx = abs(x)
+    absx2 = absx ** 2
+    absx3 = absx ** 3
+
+    return ((1.5 * absx3 - 2.5 * absx2 + 1) * (absx <= 1) + 
+        (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (1 < absx <= 2))
+
+@_vectorize_fp64
+def lanczos2(x):
+    """matlab.images.internal.resize.lanczos2"""
+
+    eps = np.finfo(np.float64).eps
+
+    return (np.sin(np.pi * x) * np.sin(np.pi * x / 2) + eps) / ((np.pi ** 2 * x ** 2 / 2) + eps) * (abs(x) < 2)
+
+@_vectorize_fp64
+def lanczos3(x):
+    """matlab.images.internal.resize.lanczos2"""
+
+    eps = np.finfo(np.float64).eps
+
+    return (np.sin(np.pi * x) * np.sin(np.pi * x / 3) + eps) / ((np.pi ** 2 * x ** 2 / 3) + eps) * (abs(x) < 3)
+
+
+def resize_weight(scale=2, kernel=None, downsample=False):
+    """A function that outputs weights for a specified resizer
+
+    This function assumes center-aligned interpolation.
+
+    Args:
+        scale: (int) Resize factor. The type of value for downscale is also an integer.
+            Default is 2.
+        
+        kernel: (str) Interpolation method.
+            Available methods: box, bilinear(triangle), bicubic(cubic), lanczos2, lanczos3.
+            Default is 'bicubic'.
+        
+        downsample: (bool) Whether the weights are used for downsample instead of upsample.
+            Default is False.
+    
+    Ref:
+        [1] P. Getreuer. (2011). Linear Methods for Image Interpolation. Image Processing On Line, 1, pp. 238–259.
+
+    """
+
+    if not isinstance(scale, int) or scale <= 1:
+        raise ValueError('\'scale\' must be an integer greater than 1!')
+    
+    if kernel is None:
+        kernel = 'cubic'
+    else:
+        kernel = kernel.lower()
+
+        if kernel == 'bicubic':
+            kernel = 'cubic'
+
+        if kernel == 'bilinear':
+            kernel = 'triangle'
+
+    if kernel in ['box', 'triangle']:
+        taps = 1
+    elif kernel in ['cubic', 'lanczos2']:
+        taps = 2
+    elif kernel in ['lanczos3']:
+        taps = 3
+    else:
+        raise ValueError('Unknown resize kernel {}!'.format(kernel))
+
+    func = eval(f'{kernel}')
+
+    if scale % 2 == 0: # scale is even
+        kernel_width = 2 * scale * taps
+        weight_1d = np.empty(kernel_width, dtype=np.float64)
+        weight_1d[kernel_width//2:] = func([(2 * i + 1) / (2 * scale) for i in range(kernel_width // 2)])
+        weight_1d[:kernel_width//2] = weight_1d[:kernel_width//2-1:-1] # mirror
+
+    else: # scale is odd
+        kernel_width = 2 * scale * taps - 1
+        weight_1d = np.empty(kernel_width, dtype=np.float64)
+        weight_1d[kernel_width//2:] = func([(i / scale) for i in range(kernel_width // 2 + 1)])
+        weight_1d[:kernel_width//2] = weight_1d[:kernel_width//2:-1] # mirror
+
+    # kernel normalization:
+    for i in range(scale):
+        weight_1d[i::scale] /= weight_1d[i::scale].sum()
+
+    if downsample:
+        weight_1d /= scale
+    
+    if kernel in ['box']:
+        weight_1d = weight_1d[scale//2:-scale//2]
+
+    return weight_1d
diff --git a/xUnit/README.md b/xUnit/README.md
index dd695e1..9feca45 100644
--- a/xUnit/README.md
+++ b/xUnit/README.md
@@ -3,7 +3,7 @@ Converted from **official** [kligvasser/xUnit](https://github.com/kligvasser/xUn
 ---
 
 ### Reference
-**[1] Kligvasser, I., Shaham, T. R., & Michaeli, T. (2018). xUnit: Learning a Spatial Activation Function for Efficient Image Restoration. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [[PDF]](https://tomer.net.technion.ac.il/files/2018/03/xUnit_CameraReady.pdf) [[Code]](https://github.com/kligvasser/xUnit)**
+**[1] Kligvasser, I., Shaham, T. R., & Michaeli, T. (2018). [xUnit: Learning a Spatial Activation Function for Efficient Image Restoration](https://ieeexplore.ieee.org/document/8578356/). In the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[PDF]](https://tomer.net.technion.ac.il/files/2018/03/xUnit_CameraReady.pdf) [[Code]](https://github.com/kligvasser/xUnit)**
 
 [2] Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://ieeexplore.ieee.org/document/7839189/). IEEE Transactions on Image Processing, 26(7), 3142-3155. [[PDF]](https://arxiv.org/pdf/1608.03981) [[Code]](https://github.com/cszn/DnCNN)