Blotches detection

HNUE JOURNAL OF SCIENCE

Natural Sciences, 2020, Volume 65, Issue 4A, pp. 51-57

This paper is available online at http://stdb.hnue.edu.vn

BLOTCHES DETECTION

Nguyen Thi Quynh Hoa

Falcuty of Information Technology, Hanoi National University of Education

Abstract. Blotch detection and removal is an important issue for archive film restoration

and it can be extended in many other field of image processing. In this work, a new

proposal including an automatic detection method and inpainting scheme is introduced.

First, a technique for automatically detecting blotches based on local changes of pixels on

consecutive frames is applied. Specifically, a two-stage Simplified Ranked Order

Difference (SROD) detector is proposed to identify blotches on frames. Next, an improved

inpainting was applied to restore the blotches so that it is undetectable by viewers. Our

proposal is executed automatically without external parameters. The proposal has been

tested on a serial of natural images with different sizes and resolutions. Experimental

results show that the proposed solution has been successfully detected with fairly high

accuracy and quite smooth restored blotches. Based on result analysis, the proposal has

many potential and applications in the future. Index Terms-blotch detection, blotch

removal, restoration, inpainting.

Keywords: Terms-blotch detection, blotch removal, restoration, inpainting.

1. Introduction

Storage and processing of visual aids that do not affect their overall quality is still a

challenging problem. Storage and handling of old movies is even more difficult due to the

physicochemical properties of the film. Indeed, many physical factors can affect the overall

quality of the old-time movie such as moisture and heat, one of the factors responsible for

the fading of old movies. One of the common issues of interest and research is the restoration of

old films damaged by various problems like grainy, fuzzy, faded and artifacts other structures.

Until now, a considerable effort has been devoted to developing methods and systems of digital

precision to restore old film. The stretch marks and scratches are some artifacts film is the

most popular study [1-2].

During long time ago, the old film was restored by one of the two most common

techniques that technical manual or semi-automatic. However, the quality of the recovery

depends on the skill of the user and the limitations of the tools and methods used. For all

these reasons, the film industry is always searching and researching the advantages of digital

solutions. Some techniques are proposed based on the model of film degradation phenomena [3].

However, these models often ignore the cognitive aspect and therefore excludes the human

observer, the final judge. Various methods for removing spots have been proposed in the

literature. Some methods based on image printing techniques [4-5]. In addition, a simple way

Received March 20, 2020. Revised May 7, 2020. Accepted May 14, 2020

Contact Nguyen Thi Quynh Hoa, e-mail address: [email protected]

51

Nguyen Thi Quynh Hoa

to expand the method to blur images into videos however it can lead to incoherent results [6]

and especially around moving objects.

The purpose of this work we propose a complete solution for detecting and removing spots

movies based on some perceived feature of the human visual system. The performance of each

component is assessed through objective research extensively tested on real video. The

perceived quality of the frames has been restored and are evaluated by fuzzy picture quality

printing for professional use.

2. Content

2.1. Monoresolution detection of suspicious regions

k)

An input image I⁽could be easily represented at different resolution levels by a Gaussian

pyramid. The spots are treated like a change of lighting locally between the degraded image

I⁽and every reference I^(k-1)and I⁽. However, earlier methods are not handling the

different size of the spot as well as the shift of different intensity, because it works directly

on the resolution of the input image. This leads to reduce the incidence of false alarms and

improve detection accuracy rate [7]. The spot detection includes the two main steps. First

identify likely areas placed first broken. Then, a heuristic detection for SROD only machine is

applied to those areas. More precisely, the suspicious region is positioned using a powerful

movement patterns to lighting variations. In this respect, we retain the affine motion model

proposed in [8] for efficiency and simplicity of it. Taking into consideration the displacement

k)

k+1)

k-1,k

d_j⁽

(r_j) of the pixel r_jbetween A_j^(k-1)and A_j^(k), and the illumination coefficient h_j^(k-1,k)that

)

reflects its intensity change between both sub-images, the observation model is defined by

ꢂ ꢄ

ꢂ

ꢁ

ꢄ

ꢂ

ꢁ

ꢄ

ꢂ

ꢁ

ꢄ

ꢂ

ꢃ−ꢇ,ꢃ

ꢀ_ꢁ^ꢃ(ꢅ ) ꢆ ℎ

(ꢅ )ꢀ ^ꢃ−ꢇꢈꢅ ꢉ ꢊ

(ꢅ )ꢋ ꢉ ꢌ

ꢁ

^ꢄ(ꢅ )

(1)

ꢃ−ꢇ,ꢃ

ꢁ

ꢂ

ꢁ

ꢄ

ꢃ−ꢇ,ꢃ

where ꢌ

is the estimation error. As these parameters are locally valid, they are estimated

in blockwise way by ther Block-Matching algorithm (BMA). Thus, the sub-image A_j^(k)is firstly

partitioned into blocks ꢍ^ꢂꢃꢄof size ℓ_ꢁ× ℓ_ꢁ∈ꢑ[1, … , ] × [1, … , ]. Then, the optimal block

ꢒ

ꢔ

ꢓ

ꢏ

ꢂꢎ ,ꢐ ꢄ

ꢏ

2

ꢂꢃ−ꢇꢄ

ꢍ

in Ǎ_ꢁ^{ꢂꢃ−ꢇꢄ}is the one that minimizes the mean squared error relatively to the considered

∗

ꢂꢎ ,ꢐ ꢄ

ꢏ

blockꢑꢍ_ꢂ^ꢂ_ꢎ^ꢃꢄ_{,ꢐ ꢄ}in a searche area ꢑꢕ^{ꢂꢃ−ꢇꢄ}

:

ꢏ ꢏ

ꢂꢎ ,ꢐ ꢄ

ꢏ

The illumination parameters are given by

ꢂ

ꢄ

ꢂ

ꢄ

,

ꢏ

ꢞ

ꢞꢟꢓ

,

ꢏ

̌

,ꢑꢙ

ꢖ

ꢗꢘ

ꢂꢑꢙ

ꢚꢛ ,ꢜ ꢝ

∀ꢅ ∈ ꢑꢍ_{(ꢎ ,ꢐ )}, ĥ_{(ꢎ ,ꢐ )}^ꢄ(ꢅ ) ꢆ

^{ꢚꢛ ,ꢜ ꢝ}ꢑ

(2)

(3)

ꢏ

ꢂ

ꢏ

ꢂ ꢄ

ꢂ

ꢃ−ꢇ,ꢃ

ꢃ

ꢁ

ꢔ

ꢠ

ꢏ

ꢄ

,

ꢞꢟꢓ

ꢢ

ꢑꢡ

,

ꢏ

ꢈꢛ ,ꢜ ꢋ

ꢏ

ꢂ

ꢣ^ꢂ

ꢑꢌ_{(ꢎ ,ꢐ}^ꢄ₎(ꢅ ) ꢆ ꢤ

ꢥ ꢑĥ_{(ꢎ ,ꢐꢑ )}^ꢄ(ꢅ )ꢤ

.

ꢃ−ꢇ

ꢃ−ꢇ,ꢃ

and

ꢂ

ꢄ

ꢂ

ꢄ

,

ꢏ

ꢞ

ꢞꢟꢓ

,

ꢏ

ꢁ

̌

ꢑꢙ

ꢏ

ꢚꢛ ,ꢜ ꢝ

ꢏ

Since most of the reported statistical tests require that analyzed data be normally

ꢂ

ꢁ

ꢄ

ꢂ

ꢃ−ꢇ,ꢃ

ꢆ ꢧꢑℎ_{(ꢎ ,ꢐ )}^ꢄꢨ

is firstly transformed by using the Box-Cox

ꢃ−ꢇ,ꢃ

distributed, the set ꢑꢦ_{ꢂ ꢄ}

ꢏ

(ꢎ ,ꢐ )

ꢏ

ꢄ

ꢂ

ꢃ−ꢇ,ꢃ

ꢩ^ꢂ

ꢆ ꢧꢑℎ_{(ꢎ ,ꢐ )}^ꢄꢨ

ꢃ−ꢇ,ꢃ

ꢁ

̃

transformation [9]. This transformation give rise to the set ꢑꢦ_{ꢂ ꢄ}

ꢏ ꢏ

(ꢎ ,ꢐ )

ꢏ

ꢄ

ꢩ^ꢂ

ꢃ−ꢇ,ꢃ

ꢁ

normally distributed. Then, in order to locate the outliers within ꢑꢦ_{ꢂ ꢄ}

, it is possible to

apply one statistical test among the ones reported [10-11]. In this work, we retain the Minimum

52

Blotches detection

Covariance Determinant (MCD) test for its efficiency and relatively low computational

ꢂ

ꢁ

ꢄ

ꢃ−ꢇ,ꢃ

complexity [10]. The MCD test provides a set ꢑꢪ_{ꢂ ꢄ}

of a typical values of the illumination

coefficient that are asssociated to candidate blocks in A_j^(k)and A_j^(k-1)and are more susceptible to

be blotched. The same procedure is carried out to detect suspicious blocks between A_j^(k)and

A_j^(k+1). Only the blocks that are judged as suspicious in both the backward and the forward

directions (by considering respectively the pairs (A_j^(k-1), A_j^(k)) and (A_j^(k+1), A_j^(k)) are retained for

the final blotch detection step since blotches are considered as illumination variations occuring

in both directions.

2.2. Blotch detection

Given the positions of the suspicious regions at different resolution levels, it is necessary

to deduce the positions of the retained candidate regions at the initial resolution level. Since

the goal behind resorting to a multiscale analysis is to handle the different sizes of the

blotches, we consider a block at the initial resolution level as suspicious if it has been judged

as such at, at least one resolution level initial resolution level. Since the goal behind resorting

to a multiscale analysis is to handle the different sizes of the blotches, we consider a block at

the initial resolution level as suspicious if it has been judged as such at, at least one resolution

level ꢫ, ∀1 ≤ ꢫ ≤ ꢬ.

The final step consists of detecting the corrupted pixels in each candidate region at the

initial resolution of ꢭ^ꢂꢃꢄ. For this purpose, one of the reported heuristic detectors as the SDI_a,

the ROD, the SROD, or the AR based detectors is used between suspicious blocks in ꢭ^ꢂꢃꢄꢑand

their homologous in the motion compensated refrence frames.

2.3. Blotch removal

We assume that the spots are identified and detected correctly in the previous step at this

stage is a binary table, where the mottled pixel is color coded white and the rest black. This

binary image is used to guide the recovery process. To remove blotches are detected, an

approach based on the proposed inpainting. The performance of the solution is assessed

subjectively or using some full reference image quality classical metrics like PSNR. In this

work, we use hierarchical diagram similar to [12] and strategic global optimization. This

approach introduces an effective performance and high quality output.

(b) bloctch detection map

(a) A blotched image

(c) The initial priority map

Figure 1. An example of the priority map

53

Nguyen Thi Quynh Hoa

Firstly, a Gaussian pyramid was built from the original image to create hierarchical

diagrams of input images. A set of images with different levels of detail can be created with

ꢮ_ꢯꢆ ꢭ as the input or original image. Number of pyramid levels depending on the original size

of the image and the minimum resolution allowed. Then, a strategy used to fill in missing areas

with the lowest resolution, ꢮ_ꢰ. Athigher resolutions, inpainting problem are modeled as a

graph optimization labels which help to show the selected label for each pixel unknown. It can

be determined by optimizing the energy function optimization algorithm by global, multi-

labeled graph cut [13]. A description of the algorithm removes spots is given in Figure 1.

2.3.1. Correction of lowest resolution frame

At lowest resolution, a template-based approach is used to remove the blob by using

priority based on the window and choose the patch. Recommended removal methods work

repeated as follows:

- Detect spots: Identify spots and their boundaries based on the binary image. If no pixels

in the spot, the algorithm is terminated due spots completely erased.

- Define priorities edit: Calculator and randomly select a pixel p with the highest priority

and determines a patch, P, gathered at p. In this work, we used the model of the most common

priorities proposed in [14, 15] Combine the patch: Find a patch is not blurred, ꢱ_ꢲ, similar to ꢱ_ꢳ

with mean squared error of pixel squares is not blurred.

- Blotch removal: Fill in missing information in patch ꢱ_ꢲ.

- Information update: Update the binary mask image and return the step 1.

Correction priority: So good priorities is essential because it directly affects the quality of

the output. In this work, we used the model of the most common priorities proposed in [15] and

it is defined as in Equation 4

ꢴ

ꢂꢳꢄ

ꢆ ꢵ_{ꢂ ꢄ}ꢶ_{ꢂ ꢄ}

ꢑ

(4)

ꢳ

where C(p) and D(p) denote confidence and data terms, respectively and they are defined as follows:

∑

ꢖ

ꢂꢷꢄ

ꢷꢸꢹ ∩ꢻ

ꢺ

ꢵ_ꢂꢳꢄ

ꢆ

(5)

(6)

|ꢼ |

ꢺ

ꢾ

ꢓ

ꢂ ꢄ

ꢶ ꢽ ꢆ _{ꢾ +ꢿ}

ꢑ

ꢔ

During the initialization process, the values of reliability, C(p) is set to 0 for each pixel in

blotched and 1 for others. ∈ is very small positive value, which ensures that terminology is

always dominate the other. ꣀ_ꢇand ꣀ₂are two positive eigenvalues (ꣀ_ꢇ≥ ꣀ₂) determine the

local changes of pixel intensities in each window Wp, gathered at p and is characterized by the

following matrix:

2

꣆꣇

ꢚ ꢝ

꣆꣈

꣆꣇ ꣆꣇

∑

ꢂ

ꢄ

ꣁ_ꢂꢳꢄ

ꢆ

ꢮ_ꣂꣃ, ꣄ ꣅ

^{꣆꣈ ꣆꣉}₂꣊ꢑ

ꢚ ꢝ

꣆꣉

(7)

ꢺ

꣆꣇ ꣆꣇

꣆꣈ ꣆꣉

꣆꣇

where ꢮ_ꣂis a Gaussian window function calculates a total weight. The term data including

ꢺ

structural features depends on the variation of two separate values. Figure 1 illustrates a

preferred map said pixels will be restored first.

Patch selection: The next step in the optimization algorithm is searching for patches

matching the blurred area. In our work, a suitable patch is determined using similar

54

Blotches detection

measurements on all pixels not faded in patches. Therefore, it is determined based on the

difference of color and gradient as below:

2

꣋

∑

ꢊ(Ψ_ꢳ, Ψ_ꢲ) ꢆ ꢚ(ꢭ_ꢳꢥ ꢭ_ꢲ) ꢉ ꣌(∇ꢭ_ꢳꢥ ∇ꢭ_ꢲ) ꢝꢑ

(8)

where I_p, I_qare the corresponding CIELab vectors; ∇ꢭ_ꢳ, ∇ꢭ_ꢲrepresent the image gradient

vectors. ꣌ is a user defined weight balancing the two terms. In our experiments, we used ꣌ ꢆ

0.5. The patch with minimal distance to the source patch, ꢱ_ꢳ, is the chosen one and given

below:

ꢱ_ꢳ̂ꢆ ꣍ꢅ꣎ꢤ꣏꣐_꣑∈Φ{ꢊ(Ψ_ꢳ, Ψ_ꢲ)}ꢑ

(9)

2.3.2. Correction for higher resolution frame

When finished creating images lowest resolution, compensation map is generated and

used to reconstruct a higher resolution. Map offset determine the relationship between the pixel

needs to be removed and the pixels in the region are not blurred are given below

ꢂ

ꢄ

ꢕꣁ_ꢂꢳꢄꢆ ꢧ_ꢂ^{∆꣈,∆꣉ꢑꢳ ꣈,꣉ ∈Ω}

(10)

ꢄ

ꢯ,ꢯ ꢑ꣒꣓꣔꣕꣖꣗꣋꣘꣕

Compensation map obtained from the lower resolution is interpolated to higher resolution.

However, the output image is derived directly from this map which contains annoying artifacts

affect the nature of the images obtained. The authors of [16] the data and smoothness to refine

compensation map. Energy function is defined as follows:

ꢂ

ꢄ

ꢂ

ꢄ

ꢂ

ꢄ

ꢂ

ꢄ

ꢂ

ꢁ

ꢄ

ꢂ

ꢄ

ꢃ−ꢇ

ꢃ−ꢇ,ꢃ

ꢃ−ꢇ

ꢑḂ

ꢆ ꣍ꢅ

ꢂ

ꢄ

′

ꢏ

ꢄ

ꢤ꣏꣐

ꢂ ꢄ

ꢞ

ꢂꢀ_ꢁ^ꢃ(ꢅ ) ꢥ ꢑℎ Ǎ_ꢁ^ꢃ−ꢇꢈꢅ ꢉ ꢊ

(ꢅ )ꢋ ꢥ ꢑꢌ_{(ꢎ ,ꢐ )}ꢄ²ꢑ (11)

∑

∈ꢑ꣙^ꢂ

꣖_ꢏ∈ꢑꢙ

ꢚꢛ ,ꢜ ꢝ

ꢏ ꢏ

(ꢎ_ꢏ,ꢐ_ꢏ)

ꢞꢟꢓ

′

ꢞꢟꢓ

(ꢎ_ꢏ^∗,ꢐ_ꢏ^∗)

ꢑḂ

ꢁ

ꢏ ꢏ

ꢚꢛ ,ꢜ

ꢝ

ꢚꢛ ,ꢜ

ꢝ

ꢏ

∑

ꢂ

꣚ꣁ ꢆ ꣛ ꣚_꣜(ꢕꣁ_{ꢂ ꢄ}) ꢉ ꢂ1 ꢥ ꣛ꢄ _{ꢳ,ꢲ ∈ꢰꢙ}꣚_꣘ꢂꢕꣁ_{ꢂ ꢄ}, ꢕꣁ_{ꢂ ꢄ}

ꢄ

(12)

ꢄ

ꢳ

ꢲ

where ꣚_꣜is a data term related to external requirements and ꣚_꣘is a smoothness term defined

over a set of neighbouring pixels, N B. The parameter a is a user defined weight balancing the

twoꢀtermsꢀsetꢀtoꢀαꢀ=ꢀ0.5ꢀinꢀourꢀexperiment.ꢀThe detail of the data term and smoothness term are

given by equation (12), (13):

∞

ꢂ꣈+∆꣈,꣉+∆꣉ꢄ∈Ω

꣚_꣜ꢂꢕꣁ_{ꢂ ꢄ}ꢄ ꢆ ꢧ

(13)

ꢳ

ꢑ꣒꣓꣔꣕꣖꣗꣋꣘꣕

ꢯ

whereꢀβꢀandꢀγꢀareꢀweightsꢀbalancingꢀtheseꢀtwoꢀterms,ꢀsetꢀtoꢀβꢀ=ꢀl,ꢀγꢀ=ꢀ2ꢀinꢀourꢀexperiment.ꢀThere

are many approaches for minimizing this function. In the proposed method, a global

optimization based on graph-cuts is developed because of the efficient implementation and the

available source code. The alpha parameter is to determine the importance, or the balance

between two operands. In this case, Ed and Es are chosen as 0.5 because these two operands are

considered to have the same role, so they are equal.

2.4. Experiment results

This section is dedicated to the performance evaluation of the proposed framework.

The performance of blotch detector scheme is evaluated in three round of simulation. The

first round shows the benefits of versatile analysis versus monochrome case as in [7]. The

second round presents a visual assessment using SROD detection in locating the damaged area

candidates. And the last round is to evaluate the performance in comparison with the proposal in [17].

ꢯ

ꢑ꣙

=꣙꣞

ꢂ ꢄ

ꢷ

ꢂ

ꢺ

ꢄ

꣚_꣘ꢂꢕꣁ_{ꢂ ꢄ}, ꢕꣁ_{ꢂ ꢄ}ꢄ ꢆ ꣝

(14)

ꢳ

ꢲ

ꢑ꣒꣓꣔꣕꣖꣗꣋꣘꣕

꣟꣠꣞(꣙꣞ )+꣡꣠꣢ꢂ꣙꣞

ꢂ ꢄ

ꢺ ꢄ

ꢂ

ꢄ

ꢺ

A sequence of thirteen frames has been used for testing. Frames are 720 × 576 pixels and

encoded, which shows some typical frames to provide a visual demonstration of our proposal.

55

Nguyen Thi Quynh Hoa

The first line contains some corrupted frames and the blotch detection is detected and

represented by binary mask frames of the second line. The blotch removal frames are shown in

the last line. In order to evaluate of performance the blotch removal algorithm, we compared

with some state-of-the-art inpainting methods such as A. Criminisi et al. [14], Wu et al. [18] and

Dang et al. [15]. The image quality of the proposed method has been objective evaluation with

unclear quality indicators [19] and shown in Table 1. The higher the result are, the better the

quality of the propose approach is.

Table 1. The blotch removal quality metric

Method

Our proposal

[15]

[14]

[18]

Frame ID

18

20

0.2159

0.1987

0.1178

0.1391

0.1078

0.1735

0.1711

0.1928

0.1934

0.2382

0.3363

0.2382

0.1429

0.199

0.2

0.2118

0.2022

0.1234

0.1286

0.1102

0.1705

0.1577

0.1825

0.1946

0.242

0.2182

0.2032

0.1095

0.1321

0.1135

0.1773

0.1727

0.1844

0.1939

0.2367

0.3227

0.2367

0.1418

32

0.1105

0.1248

0.1075

0.1724

0.1591

0.182

37

46

50

68

85

94

0.1934

0.2436

0.3456

0.2436

0.1399

102

103

111

118

0.3422

0.242

0.1395

3. Conclusions

The article has proposed a framework to detect and restore spots. The framework consists

of two main stages: detection and recovery. For spot detection, a simple rank order difference

(SROD) detector is proposed. Next, spots will be restored based on improved dimming printing

techniques. The test results show an outstanding performance and expected results. The overall

execution time of the schema is completely acceptable. In the future, we will improve and

upgrade the framework to make it better with larger resolution videos.

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