A Basic Tool for Improving Bad Illuminated Archaeological Pictures

Michela Lecca

Fondazione Bruno Kessler, Digital Industry Center, Technologies of Vision, via Sommarive 18, Trento 38122, Italy

Keywords:

Image and Contrast Enhancement, R et inex Theory, von Kries Model, Archeological Images.

Abstract:

Gathering visual documentation of archaeological sites and monuments helps monitor their status and pre-

serve and transmit the memory of the cultural heritage. Good lighting is essential to provide pictures with

clear visibility of details and content, but it is a challenging task. Indeed, illuminating a site may require

complex infrastructures, while uncontrolled lights may damage the artifacts. In this framework, computer

vision techniques may greatly help archeology by relighting and/or improving the images of archaeological

objects that cannot be acquired under a good li ght. This work presents MEEK, a basic tool to improve low-

light, back-light and spot-light images, increasing the visibility of their details and content, while mitigating

undesired effects due to illumination. MEEK embeds three algorithms: the Retinex inspired image enhancer

SuPeR, the backlight and spotlight image relighting method REK, and the popular contrast enhancer CLAHE.

One or more of these algorithms can be applied to the input image, depending on the light conditions of the

acquired environments as well as on the ﬁnal task for which the image is used. Here, MEEK is tested on many

archaeological color pictures with bad light showing good performance. The code of MEEK is freely available

at https://github.com/MichelaLecca/MEEK.

1 INTRODUCTION

Archeology enables us to le arn about the past an d

build the future based on the experience of our an-

cestors. Preserving and monitoring the condition

of ancient artworks, like archaeological sites, paint-

ings, mosaics, monuments, is the key to passing on

a wide cross-section of hum a n knowledge to future

generations. In this context, computer vision tech-

niques can be of great help in collecting visual d oc-

uments of imp ortant past artifacts, classifying them

accordin g to their visual features, monitoring sites

from satellites, as well as planning non-intr usive ac-

tions for the ir con servation and renovation (van der

Maaten et al., 2006), (Brutto and Meli, 2012), (Trav-

iglia et al., 2016), (Engel et al., 2019), (Resler et al.,

2021), (Monna et al., 2 021). For all these tasks, good

light conditions are essential to obtain pictures wher e

the content and the details of the objects of interest

are clearly visible. Nevertheless, in general, such a

requirement is hard to be satisﬁed and may need for

complex, expensive infrastructures. This is th e case

with objects positioned in hard-to-reac h places and

paintings th at can be da maged by uncontrolled lights.

In this context, image enhancement technique s pro-

vide non-invasive so lutions to recover better, global

https://orcid.org/0000-0001-7961-0212

and local visibility of the content a nd details of the

acquired scene.

This work presents a basic too l for enhancing pic-

tures captured under low-light, backlight and spot-

light. Low-light is weak illumination that produces

dark pictures, while back light and spotlight are highly

non-uniform lights that gene rate images with both

dark and bright region s. All these illuminations are

common in archaeological environments. For in-

stance, low-light is typical of excavations and crypts;

backlight is usual in churches and castles, where win-

dows/celling roses and small slits or crevices let in

an intense but not diffused light; spotlight is present

in places with works in progress or near break-

able, delicate stuff, where an artiﬁcial source high-

lights speciﬁc objects while obscuring the rest of

the scene. The presented tool, called MEEK from

the key-expression iMage EnhancEment Kit, emb eds

three general purpose image enhancers that can be

used individually or combined togethe r to improve

the quality of the input image. These enhancers are

the Retinex in spired method SuPeR (Lecca and Mes-

selodi, 20 19), the ba c klight image enhancer REK

(Lecca, 2022b), and the well known contrast enhancer

CLAHE (Zuiderveld, 1 994). These algorithms have

been chosen among many others because o f their low

complexity and low numbe r of parameters (one per

204

Lecca, M.

A Basic Tool for Improving Bad Illuminated Archaeological Pictures.

DOI: 10.5220/0011648800003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP, pages

204-211

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

algorithm ), as well as their p erformance. In partic-

ular, both SuPeR and REK enhance the input image

by a pixel-wise, non linear rescaling of the intensity

values of the image channels. Precisely, SuPeR sam-

ples a set of high intensity pixels over each channel

and uses the m to process the colors of the other image

pixels increa sin g the brightness and contrast of th e in-

put image, while mitigating possible chromatic dom-

inants of the ligh t. REK is a fusion-based enhancer

speciﬁcally d esigned for backlight and spotlight im-

ages. It combines th e input image with an over-

enhanced version of it through a weighted sum so that

the dark regions are improved while the bright ones

are preserved. D ifferently from SuPeR, REK does

not change the image chro maticity, i.e. the light color

is neither removed nor attenuated. Finally, CLAHE

stretches the d istribution of the image brightness (or

of the image channels, dep ending on the implemen-

tation) to incre ase the image c ontrast while limiting

the ampliﬁcation of possible noise. As already men-

tioned above, these algorithms can be used individ-

ually o r applied sequentially upon the input image.

For instance, a backlight image can be processed ﬁrst

by REK to improve the dark a reas, then by SuPeR

to smooth possible chromatic casts of the light, and

ﬁnally by CLAHE to further enhance the contra st.

Here, the algorithms o f MEEK and some combina-

tions of them have been tested on many real-world ar-

chaeological images and discussed on some relevant

examples. The code of MEEK is released for free on-

line (Lecca, 2022a).

2 MEEK

This Section describes the three algorithms included

in MEEK and the MEE K interface and usage.

2.1 SuPeR

SuPeR (Lecca and Messelodi, 2019) is an image en-

hancer inspired by the Retinex theory (Land et al.,

1971). As Retinex, SuPeR takes as input a color im-

age J, processes pixel-wise its color channels inde-

pendently, and re turns a new image, in which bright-

ness and contrast are increased, the color distribution

is more unifo rm and possible dominants of the light

are mitigated or even removed.

Speciﬁcally, SuPeR partitions J from a regular

grid with n tiles T

, . . . , T

, where n > 1 is an u ser

input. For each tile, SuPeR compu tes the barycen-

ter b

of T

, and for each cha nnel I it computes the set

T (I) of the n pairs (b

) w here I

the maximum value

of I over T

. Then, for any pixel x of I, SuPeR maps

I(x) on a new value S(x) given by

S(x) =

I(x)

w(x)

(1)

where w(x) is a stric tly positive value computed from

T (I). Zero division is of course pr evented. Pr e cisely,

w is given by:

w(x) =









∑

)∈B

(x)

δ(x,b

)

∑

)∈B

(x)

δ(x,b

)



−1

if B

(x) 6=

I(x) otherwise

(2)

where B

(x) con tains the pairs of T (I) whose intensity

exceeds I(x), i.e.:

(x) ⊆ T (I) = {(b

) ∈ T : I

> I(x)} (3)

and

δ(x,b

) = 1 −

k x − b

+ ε. (4)

In this last equation, k · k indicates the L

norm, D is

the length of the diagonal of the image support and ε

is a small, strictly positive value introduced to prevent

division by zero. S is then remapped to range over

{0, . . . , 255}. The three proce ssed channels are th en

packed into a new RGB image.

It is to note that re-working I(x) based on both

color and sp a tial features is a distinctive trait of

Retinex and is faithful to some aspects of the human

vision system. Moreover, since S(x) is a linear com-

bination of the ratios I(x)/I

s, it is robust to changes

of illumin ation. In fact, according to the von Kries

model, in a digital imag e , any change of color due

to a change of light is well approximated by a linear

diagona l tran sf orm (Finlayson et al., 19 94), (Lecca,

2014). Consequently, local intensity ra tios and their

linear combinations are invariant against illuminant

variations.

The name ’SuPeR’ comes from the fact that this

algorithm extracts the visual and spatial information

relevant to image enhancement from blocks of pix-

els (i.e. the tiles), that are treated as super-pixels and

each of them is represented by a po sition (i.e. the tile

barycenter) and an intensity value (i.e. the maximum

intensity over the tile).

2.2 REK

The algorithm REK (L ecca, 2022b) takes as input

a color image J with strong backlight or spotlight

and up-scales its color channels by a value α strictly

greater than o ne, i.e. J is mapped onto image K by

the following equation:

K(x) = αJ(x). (5)

A Basic Tool for Improving Bad Illuminated Archaeological Pictures

205

According to the von Kries model, this up-scaling op-

eration br ightens up the dark regions, increa sin g the

visibility of their details and content, but at the same

tim, it may over-enhance the bright regions. To avoid

over-enhancemen t, REK fuses J and K into a new im-

age R deﬁned as:

R(x) = (1 −W (x))J(x) +W(x)K(x), (6)

where W is a weigh ting f unction, that ranges over [0,

1] and pe nalizes (awards, resp.) the intensities of the

dark ( bright, resp.) pixels of J, while awards (penal-

izes, resp.) those of K. Precisely,

W (x) =



1 −

U(x) − m

M − m



(7)

where U is the image luminance, i.e.

U(x) = 0.299I

(x) + 0.587I

(x) + 0.114I

(x), (8)

, I

and I

are the red, green and blue image chan-

nels, m and M are the minimum and maximum values

of U and p is a strictly positive, user parameter, con-

trolling the shape o f W .

Experiments p resented in (Lecca, 2022b) indicate p =

3 and p = 5 as suitable values for a good enhance-

ment. Regarding the parameter α, REK estimates its

value from U as follows:

α =

− σ

, (9)

where B and D contain respectively the pixels with U

greater than threshold τ and th ose with U smaller or

equal than τ, τ =

M− m

, while µ and σ d enote respec-

tively the mea n value and the standard deviation of the

set in the subscript. Within this estimate , REK pushes

the luminance of the dark regions towards that of the

bright regions without over-enhancing D. Anyway, it

is to note tha t when the bright region is close to white,

the standard variation δ

is close to zero and thus µ

is mapped to µ

: in this case, there is the r isk of sat-

urating some pixels in the dark regions and manual

intervention is needed to lower the value of α.

2.3 CLAHE

Increasing the visibility of image details is very im-

portant in archeology to visua lize and describe lo-

cal structures like inscriptions, mosaic tiles, signs of

erosion possible present on surfaces. This task can

be achieved by histogram equalization (HE), that re -

works the distribution of the image luminance (or o f

In some implementations, like that described in

(Lecca, 2022b), U(x) is deﬁned as the mean value of the

channel intensities at x, instead of a weighted sum of them.

This difference generally does not affect the ﬁnal result.

the image colors, depending on the implementation )

to obtain a new image with ﬂatter distribution and

higher contrast. For sake of simplicity, consider the

luminance L. HE computes the histogram h of L, nor-

malizes it so th at h ranges over [0, 1], a nd applies to

the image intensity values k = 0, . . . , Z − 1 the follow-

ing transformation:

T (k) = ﬂoor



(Z − 1)

∑

j=0

h( j)



, (10)

where Z is the number of p ossible intensity values

(usually 256) and function ﬂoo r rounds down its argu-

ment to the nearest in teger value. Better performa nce

is reached by the so-called adaptive methods, which

basically apply HE over multiple image patches in or-

der to enhance local con trast. One drawback of these

methods is the ampliﬁcation o f possible image noise,

especially in near-uniform areas, that have a peaked

histogram. The popular Contrast Limited Adaptive

Histogram Equalization (Zuiderveld, 1994) (CLAHE)

mitigates this effect, by clippin g the histogram at a

predeﬁned thre shold c (called the clip limit) before

to com pute T . The values exceeding the clip limit

are re-distributed equally among the histogram bins,

so that the integral of h over the intensity levels re-

mains equal to 1, while h becomes ﬂatter. Function T

is then applied by considering as h the new, clipped

histogram. The lower c, the slighter the distribution

stretching is and the less evident the enhancement of

the contrast is.

Figure 1: Interface of MEEK: on left, an image (from

Ravenna-Set), and on right its enhancement by SuPeR fol-

lowed by CLAH E.

2.4 MEEK Interface

MEEK (Lecca, 2022a) is implemented in C++

exploiting the image processing library OpenCV

(https://opencv.org/). After c ompilation, MEEK can

be used from a shell with the following syntax:

meek <input_image> <parameter.txt>

where

meek

is the executable ﬁle,

the input image and

<parameter.txt>

is a text ﬁle

containing the values of the parameters of the three

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

206

algorithm s, i.e. n fo r SuPeR, p and α for REK and

c for CLAHE. Setting α = −1 enables the automatic

estimation of α.

Figure 1 shows an example of the MEEK inter-

face. The ﬁrst three buttons at the bottom must be

pressed to run SuPeR, REK and CLAHE. The en-

hancement result will be displayed to the right of the

input image. The button ’Reset’ allows re-starting

the enhancement on the input image. Combination

of enhancement can be done by pressing sequentially

the buttons of the corresponding algorithms. For ex-

ample, to apply Su PeR and then CLAHE, the user

presses ﬁrst the SuPeR button, waits for the result and

then presses the CLAHE button. T he ’Save’ button

allows to save the result in the same d irectory of the

MEEK executable ﬁle. Finally, the ’Quit’ button stops

the program and closes the window. In this imple-

mentation, CLAHE is applied on the luminance im-

age c hannel, i.e. on the L component of the image

represented in the Lab color space.

3 RESULTS

MEEK has been tested on 155 color indoor pic-

tures, grouped in three datasets named Trento-SASS,

Ravenna-Set and Backlit-Set.

Trento -SASS consists of 80 images, with size

1504 × 1004, portraying the rests of the ancient, ro-

man city of Trento (Tridentum), which was broug ht

to light during the restoration of the Social Theater o f

Trento between 1990 and 2000. These rests occupy

a wide space of 1700 squared meters about and are

located a few meters below the current level of the

city. Due to their locatio n, the images of such ruins

are low-lighted and/or present moderate backlight and

spotlight. The adjective ’moderate’ indicates that the

gap between the dark and the bright regions is neither

too small nor too high, in particular for most images

the luminance is low also on the bright regions (see

Figures 2 and 3(right) for some examples).

Ravenna-Set contains 75 images, with size 720 ×

576, taken by the author in Ravenna (Italia), sp ecif-

ically in the Mausoleum of Galla Placidia (ﬁrst half

of the 5th century AD) and in some churches with

frescoes and mosaics, such as the Basilica of San Vi-

tale (530 AD a bout), the Basilica of San Giovanni

Evangelista (420 D C about) and the Basilica of San

Francesco (13th century). Also these pictures are

low-lighted and have moderate backlight/spotlight. In

particular, the images captured in the Mausoleum of

Galla Placidia are very da rk, because no lights and no

camera ﬂashes were allowed (see Figure 3, left).

Backlit-Set contains 12 strong backlight images,

partly downloa ded from the free repositories pexels

and pixabay

, and partly acquired by the author (see

Figure 4). Nine picture s depict windows roses, while

three others show monum e nts acquired against sky.

These images are used to assess the performance of

REK in comparison with SuPeR.

All the images from these datasets have been pro-

cessed by SuPeR, REK, CLAHE, SuPeR followed by

CLAHE an d REK followed by CLAHE.

The performance of SuPeR, REK, CLAHE and

of their combination s considered here is assessed by

three numerical, objective m easures, related to the hu-

man perception and usually modiﬁed by en hancing,

i.e.:

1. The mean image brightness f

, i.e. the mean value

of the sum of the three color channels. Indicated

by I

, I

and I

the color channels of an image

J, the brightness of J is d eﬁned pixel by pixel

as b(x) =

(x)+I

(x)

and f

is the average o f

b over the number of pixels; f

is related to the

global visibility of the image content;

2. The mean, multi-r esolution image contrast f

(Rizzi et al., 2004) , i.e. the me an value of the L

distance among any value b(x) and its 8 neighbor-

hoods, computed at different scales; f

captures

local and global variations of b and is related to

the detail visibility;

3. The index of the distributio n ﬂatness f

, which

is the L

distance between the probability density

function of b and the uniform probability density

function; f

is r elated to the image colorfuln ess.

In case of almost uniform low-light or moderate

backlight/sp otlight, the values of f

and f

should in-

crease after enhance ment, because the overall image

is brig htened up and its content and details be come

more visible. On the contrary, the value of f

should

decrease: in fact, the enhancer restores the visibility

of the content and details in dark areas and allows

their color tones to be better distinguished. Conse-

quently the brightness distribution ﬂattens and f

be-

comes smaller. In case of backlight/spotlig ht, the f

behave differently depending on the regions in which

they are c omputed, i.e., the whole image , brig ht re-

gions, a nd dark regions. On the bright regions, the

s are expected to remain stable since these regions

do not need enhancem ent, while on the dark regions,

and f

are expected to increase (these regio ns

becomes brighter and more c ontrasted after enhanc-

ing) and f

is expected to decrease (the b rightness

histogram, initially peaking to left, ﬂattens with en-

https://www.pexels.com/it-it/

https://pixabay.com/

A Basic Tool for Improving Bad Illuminated Archaeological Pictures

207

Enlargement of the inscription

Figure 2: Enhancement of an image from Trento-SASS and enlargement of a part (input version and enhancement by Su-

PeR+CLAHE).

hancement). On the whole image, f

and f

should in-

crease and decrease, respectively, due to the improve-

ment of the dark areas. The behavior o f f

is more

complex and d epends on the level of enhan c ement of

dark areas. In fact, an enhancer increases the con-

trast of dark regions, but in this way it decreases the

contrast between dark and bright regions. Depending

on the proportion of dark and bright area s and their

distribution in the image, the f

value calculated over

the entire image may increase, r emain stable, or even

decrease, and thus it is irrelevant for enhancer eval-

uation. Ther e fore, on backlight/spotlight images, the

enhancer perform ance are here assessed separately on

dark and bright region s.

Finally, it is to note that the exact amount of the

s depen ds on the image content. Moreover, for a

fair evaluation, the measures f

, f

and f

must be

evaluated together. In fact, the analysis of a sing le

measure usually does not provide an accurate assess-

ment of the enhancement, since for example a high

value of f

could correspond to a total saturation of

the image: in this case, checking the values of f

and

help to better describe and understand the enhancer

performance.

Tables 1, 2, 3 show the values of f

, f

and f

the input and on the enhanced images from Trento-

Table 1: Performance of MEEK on Trento-SASS. The ar-

rows indicate the expected t rend of the measures. T he pa-

rameters have been set empirically as follows: n = 144, p =

5, c = 4, while α has been automatically estimated by REK.

Algorithm f

[×10

−3

]

ր ր ց

INPUT 64.01 12.79 3.94

CLAHE 94.07 26.94 2.19

SuPeR 110.66 16.31 2.92

SuPeR + CLAHE 118.27 31.20 1.45

REK 80.45 12.20 4.14

REK + CLAHE 111.41 29.14 1.76

Table 2: Performance of MEEK on Ravenna-Set. The ar-

rows indicate the expected trend of the measures. Here, p

and c have been set like in Trento-SASS, while n has been

ﬁxed to 100 and α has been computed by REK for all the

images apart from four i mages, for which α has been set to

2.5 because the bright regions were almost white.

Algorithm f

[×10

−3

]

ր ր ց

INPUT 74.37 14.06 4.14

CLAHE 96.76 27.44 2.30

SuPeR 126.92 17.77 3.31

SuPeR + CLAHE 122.25 31.79 1.63

REK 89.26 13.28 4.33

REK + CLAHE 105.21 27.08 2.36

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

208

Table 3: Performance of MEEK on Backlit-Set broken

down by bright and dark regions. The symbols under the

measures indicate the expected trend of the measures. The

algorithms’ parameters have been set like for Trento-SASS.

(a) Assessment on Bright Regions

Algorithm f

[×10

−3

]

≃ ≃ ≃

INPUT 181.09 44.02 4.45

CLAHE 199.99 40.67 4.75

SuPeR 202.42 41.68 4.96

SuPeR + CLAHE 213.65 38.04 5.09

REK 181.76 36.30 4.52

REK + CLAHE 203.86 35.81 4.94

REK + SuPeR 203.96 32.67 5.09

REK + SuPeR + 214.74 35.95 5.10

CLAHE

(b) Assessment on Dark Region s

Algorithm f

[×10

−3

]

ր ր ց

INPUT 32.20 13.16 5.10

CLAHE 66.01 23.57 3.20

SuPeR 50.14 16.84 4.20

SuPeR + CLAHE 87.29 28.09 2.38

REK 61.84 15.35 4.53

REK + CLAHE 96.32 29.90 2.13

REK + SuPeR 87.53 19.22 3.61

REK + SuPeR + 105.11 32.02 1.86

CLAHE

SASS, Ravenna-Set and Backlit-Set (in this last case,

the measures are broken down by dark and bright re-

gions). All these values ar e averaged over the number

of images per d ataset.

From Tables 1 and 2 it comes that for CLAHE, for

SuPeR and for the combinations SuPeR + CLAHE

and REK + CLAHE, the tren d of the measures is

as expecte d: f

and f

increase, w hile f

decreases,

meaning that input images are brightened, their con-

trast is increased, while the luminance histogram is

ﬂattened. On both the datasets Trento-SASS and

Ravenna-Set, REK returns the worst results, because

most of these images have low-light or moderate

backlight/sp otlight and REK is speciﬁcally de sig ned

for strong backligh t and spotlight. Therefore, on

Trento -SASS and Ravenna-Set, REK increases f

less

than the other me thods do, and slightly decreases ( in-

creases, resp.) f

( f

, resp.). Much better results

are obtained by REK on Backlit-Set, whose images

present strong backlight. On this dataset, REK ha s

been also combined with SuPeR in order to mitigate

or even remove possible color casts. Tables 3(a) and

3(b) report the objective measures on the sets B and

D of the bright and dark regions. For sake of clarity,

the measures computed on B and D have been indi-

cated respectively by f

s and f

s (i = 0,1,2). REK

improves all the values f

s (i.e. it increases f

and

, while decreases f

), while maintains the triplet

( f

, f

) closer to the original one than the other

algorithm s in terms of L

distance. Indeed, th e other

algorithm s tend to over-enhance the b right areas: they

generally increase very mu ch their luminance while

worse the ﬂatness distributio n index. Combined with

CLAHE, REK and REK+SuPeR o utput good results,

but the br ight regions are less preserved than when

only REK is used.

Figures 2, 3 a nd 4 illu stra te the behaviour of the

different algorithms on some archeological images

from the three datasets, enabling the user a quick vi-

sualization of the enhancement results.

Figure 2 shows an image of the ancient city Tri-

dentum. The image has a low, yellowish light and the

characters of the in scription depicted in the middle are

poorly readable in part because of the light and in part

because of the time, that smoothed the stone surface.

REK poorly changes the image quality, while SuPeR

returns a brighter image. CLAHE r e inforces the edges

and the characters, but these latter are still not well

readable. The combination of SuPeR and CLAHE re-

turns the best result, where the light color is lowered

and the inscription becomes clearer, as shown in the

enlargement.

Figure 3 shows two images, one captured in the

Mausoleum of Galla Placid ia and the other in the an-

cient city of Trento. Both the images have been ac-

quired under low-light with a moderate backlight. All

the enhancers brighten up the scene very much, but

the best results are obtained by SuPeR and REK com-

bined with CLAHE, which reinforces the improve-

ment of the contrast. Again, differently from CLAHE

and REK, SuPeR enables the removal of th e yellow-

ish chromatic dominant of the ligh t.

Figure 4 shows the enhancement obtained by

CLAHE, SuPeR and REK on a strong backlight im-

age from Backlit-Set. In this case, CLAHE perfo rms

poorly: it remarkably increases the contrast, improv-

ing the de ta il visibility, but the overall content is not

well visible. REK provides here the be st result and

also rema rkably outperfo rms SuPeR. In fact, on this

image, SuPeR divides the intensities of the dark pix-

els by the much greater maximum intensities of the

bright tiles. Despite penalized by the spatial distance

weights, these bright intensities heavily contribute to

the ﬁnal value of S on the dark regions, th a t remain

still dark. Using a higher value of n may provide bet-

ter results, as well as conside ring alternative w eights

(see for instance (Lecca ., 2021)), but this tuning is

usually hard, especially for non-expert users. In this

context, REK offers a simpler and more c omputa-

tional efﬁcient enhancement method with much better

results.

A Basic Tool for Improving Bad Illuminated Archaeological Pictures

209

Figure 3: Examples of enhancement by MEEK on images from Ravenna-Set (on left ) and from Trento-SASS (on right).

Figure 4: Examples from Backlit-Set. On top, image en-

hancement by CLAHE, SuPeR and REK; on bottom, image

enhancement by REK, SuPeR and REK+SuPeR.

4 CONCLUSIONS

This work presented MEEK, i.e. a new, basic tool for

the enhanceme nt of im ages captured under uniform

and non- uniform low-lig ht, strong backlight/spotlight

and colored light. Such difﬁcult light conditions

are typical of archeological environments and rep-

resent a bottleneck for collecting high quality v i-

sual documents of these places. The experiments

carried ou t o n archeological ima ges of excavation s,

church e s, ro se wind ows, mosaics, frescoes show that

the three enhancers (SuPeR, REK and CLAHE) in-

cluded in MEEK a nd their c ombinations effectively

improve the quality of su c h images. In particular,

SuPeR is suitable for increasing the content and de-

tails visibility of images with low-light and moder-

ate back light/spotlight. I n addition, it attenuates or

even eliminates possible chromatic dominants of the

light. REK works well on images with strong back-

light/spotlight, improving the visibility of details and

content of the dark areas without over-enhancing the

bright ones. Combining REK and SuPeR improves

dark areas while diminishes possible light color casts.

CLAHE increases the image contrast by modifying its

color distribution. In this way, it improves the v isibil-

ity of the image details. Coupling CLAHE with the

other enhancers generally fur ther increase their per-

formance.

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

210

MEEK is only a ﬁrst step in building an effective,

easy-to-use, and more comprehensive tool for archae-

ological ima ge enhancement. In fact, MEEK cur-

rently offers a gener ic image enhan cement tool, but

it co uld be complemented by alternative and/or ad-

ditional algorithms adapted to archaeolo gical image

processing. In particular, MEEK c ould be expanded

to include denoising techniques, which are often de-

sired to reduce noise du e to low illumination. In this

context, a collaboration with archaeologists would be

of considerable help both in testing the curre nt ver-

sion of meek as well as in indicating possible modiﬁ-

cations and/or guiding the development of new ad-hoc

techniques for the enhance ment of visual documents.

MEEK c ould also be eq uipped with deep learning

image enhancers, e.g., (Liu et al., 2021), (Lv et al.,

2021), possibly trained on archaeological images. Fu-

ture research will therefore address these topics.

ACKNOWLEDGMENTS

The author would like to thank the Research Unit 3D

Optical Metrology of the Fondazio ne Bruno Kessler

of Trento (IT) and the Soprintendenza per i Beni

Culturali di Trento for providing the images of the

Trento -SASS dataset

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