US 10,311,560 B2 · Method and system for estimating blur kernel size

Abstract

A method for estimating a blur kernel size, the method including: (1) pre-processing a blurred image, to obtain an image, so that a size of the image satisfies an image input size of a multi-class convolutional neural network (CNN); (2) inputting the image into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector; and (3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image is the blur kernel size corresponding to a largest element. The invention also provides a system for estimating a blur kernel size. The system includes an image pre-processing module, a training-set synthesizing module, a multi-class CNN module, and a blur kernel size estimation module.

Claims9

Dependent claims

A method for estimating a blur kernel size, the method comprising:

(1) pre-processing a blurred image B, to obtain an image B0, so that a size of the image B0 satisfies an image input size of a multi-class convolutional neural network (CNN);
(2) inputting the image B0 into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector;
and
(3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image B is the blur kernel size corresponding to a largest element.

1 more element

A system for estimating a blur kernel size, the system comprising:

an image pre-processing module, which is used for pre-processing a blurred image, by using a down-sampling approach to obtain an input image, when the size of the blurred image is smaller than N times of the image input size M of a multi-class CNN, otherwise by using a size interception approach to intercept an image containing blurred information to get an input image;
a training-set synthesizing module, which is used for generating a training image set for training demands
T={t1, . . . , tn}, n=u×v×m,
where, T represents a two-dimensional convolution operation of randomly collected natural scene images in number of u and randomly generated blur kernels in number of v×m;
u represents the number of images;
m represents size classes of blur kernels;
v represents the number of blur kernels in each size class;
blurred images generated by the blur kernels in the same size class is labeled as an image category;
and then inputting the training images obtained by the two-dimensional convolution operation into the image pre-processing module to obtain input images, so that the sizes of the input images satisfy the input image size of the multi-class CNN;
a multi-class CNN module, which is used in the training, for sequentially inputting the training image set T={t1, . . . , tn}, n=u×v×m into the multi-class CNN, to obtain a probability distribution sequence vector of each blur kernel size class:
P(Bsize=(s1, . . . , sl, . . . , sm)|T), and adjusting the weight of each layer of the network, so that the element corresponding to the blur kernel size class sl in the training image set T is the largest element in the distribution sequence vector;
and after completion of the training, inputting an image B0 into the multi-class CNN, to obtain a probability distribution vector as follows:
P
⁡
(
B
size
=
(
s
1
,
…
⁢
,
s
m
)
|
B
0
)
=
e
(
ω
(
1
,
⁢
…
⁢
,
⁢
m
)
soft
-
max
)
T
⁢
x
full
-
connect
⁢
⁢
(
B
0
)
∑
t
=
1
m
⁢
⁢
e
(
ω
m
soft
-
max
)
T
⁢
x
full
-
connect
⁢
⁢
(
B
0
)
=
(
p
1
,
…
⁢
,
p
m
)
,
where, ωisoft-max represents weight of a soft-max layer;
xfull-connect( ) represents an output of a full-connect layer;
T represents matrix transpose;
m represents the number of blur kernel size classes;
each element in the obtained blur-kernel-size probability distribution vector represents the probability that the blur kernel size of the blurred image B is the blur kernel size corresponding to that element;
and
a blur kernel size estimation module, which is used for blur kernel size estimation, and for comparing each element in the probability distribution vector, so that the estimated blur kernel size of the blurred image B is the blur kernel size corresponding to a largest element.

105 more elements

Dependencies are inferred from the claim text.

Facts

Application

15/709,454Filed 19 Sep 2017 · continuation in part

Priority

CN 2016 1 08060727 Sep 2016

Published

US 2018/0068430 A18 Mar 2018

Granted

4 Jun 2019

Term ends

Not estimatedThe source records don’t support an estimate.

Classification20 codes

G06T 5/73
Image enhancement or restoration: deblurring; Sharpening
G06F 17/11
Complex mathematical operations: for solving equations , e.g. nonlinear equations, general mathematical optimization problems
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…
G06N 3/02
Computing arrangements based on biological models: neural networks

Method and system for estimating blur kernel size

Life of this patent

Method and system for estimating blur kernel size

Life of this patent

Abstract

Claims9