Transition-constant Normalization for Image Enhancement
Authors: Jie Huang
Man Zhou
JingHao Zhang
Gang Yang
Mingde Yao
Chongyi Li
Zhiwei Xiong
Feng Zhao
Venue: NeurIPS 2023 spotlight (2023)
Keywords: Image Enhancement
Normalization
Image Restoration

Abstract: Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks.
Although image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. 
To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks.
Specifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint.
TCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs.
We provide various formats to utilize TCN for image enhancement, including seamless  integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures.
Through extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements.
Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation.
The code is available at  \textit{\textcolor{blue}{https://github.com/huangkevinj/TCNorm}}.

Transition-constant Normalization for Image Enhancement
Jie Huang'; Man Zhou 1 Jinghao Zhang', Gang Yang', Mingde Yao', Chongyi Li Zhiwei Xiong', Feng Zhao
University of Science and Technology of China, China
Nanyang Technological University, Singapore Nankai University, China fhj0117, manman, jhaozhang, mdyao, yg1997]0mail., ustc.edu... lichongy125@gmaill com, {zwxiong, fzhao956) edu.
ggs19rr/maai.uucc.dduu.oom cn,
Abstract
Normalization techniques that capture image style by statistical representation have become popular component in deep neural networks. Although image enhance- ment can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks. Specifically, it consists two streams of normalization operations arranged under an invertible constraint, along with feature sub-sampling operation that satisfies the normalization con- straint. TCN enjoys several merits, including being parameter- free, plug-and-pll and incurring additional computational costs. We provide various formats to uti- lize TCN for image enhancement, including seamless integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures. Through extensive experiments on mul- tiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements. Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation. The code is available at https://githuub.com/ hotp://ubbooouuuuue////////Cooo
Introduction
Image enhancement is an important task in machine vision, which aims to improve the quality of low-visibility images captured under unfavorable light conditions (i.e., low light) by adjusting contrast and lightness. The last decades have witnessed quantities of approaches designed for image enhancement based on various hand-crafted priors [1 2 5) However, the complex and variant adjustment procedures make it a challenging group of tasks. In addition to the common low-light image enhancement, efforts have also been directed toward solving image enhancement-like tasks, including exposure correction, image dehazing, and SDR2HDR translation.
Very recently, the deep-learning paradigm exhibits remarkable success in the image enhancement field than traditional methods (6, 88. Despite the progress, most of them focus on roughly constructing complex deep neural architectures and have not fully explored the intrinsic characterizes of lightness in networks. In fact, the lightness variants could bring difficulties to their learning procedures. This
Botth authors contributed equally this research.
CCorresonniig author.
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Underexposure
Normalized Image
Underexposure underespasare Overexposure overexposure
Inverse
Forward used)
Transt all information X
IN
Normalzation (eg H0)
PSNR 6.07 dB
PSNR 26.66dB
Transiisnninammtar of son-innartidee
Transil all information of of
IN
TCN
Oerexpoouure
Normalized Image
Transtion constant ofmerttble
(a) Apply normalization for different exposure images.
Transition-constant property of normalization.
Figure I: In (a), the instance normalization (IN) captures lightness consistency representations across exposures, thus bridging their distribution gaps as shown I-SNE. (b), normalization techniques meet vransitinniinconsann problem, while proposed TCN exhibits differently with invertible ability.
motivates us to delve into the working mechanism of current neural networks that learn lightness adjustment and prescribe the right medicine customized for image enhancement.
On the other hand, normalization family such batch and instance normalization, is specially designed for promoting the learning procedure for deep networks. It involves computing statistical representation and normalizing the corresponding distribution, which has been shown capture image style through statistical representation (9. Image enhancement, which aims to restore lightness- corrupted images to their normal versions, can be viewed as a style transformation inherently linked to normalization techniques (see Fig. (a)). However, existing methods have rarely explored the potential of the normalization technique. Inspired by the inborn connection, we thus focus on developing the normalization technique tailored for image enhancement.
In this work, we propose novel operation called transition-constar normalization (TCN) for image enhancement tasks. The TCN operation aims to normalize partial representations to ensure consistent learning while preserving constant information for image reconstruction. As illustrated in Fig. (b), we construct the TCN within an invertible format to enable seamless information transmission to subsequent layers for reconstructing enhanced results. The TCN is designed with two key rules (see Fig. 2): (1) We organize the operations in the normalization layer into two streams, following the principle of invertible information transmission, thereby maintaining constant information transition. (2) We incorporate a subsampling operation that divides the features into two streams with consistent statistical properties. One stream provides the statistics for normalizing the other stream, satisfying the normalization requirement. Notably, the TCN requires parameters, making it convenient and orthogonal addition to existing enhancement architectures for improving their performance.
To facilitate its application, we present multiple usage formats for the TCN in image enhancement: (1) Integration into existing enhancement networks, allowing for seamless incorporation and perfor- mance improvement: (2) Plug-in capability in encoder-decoder architectures for downsampling and recomposition of information; (3) Construction of lightweight architecture based on TCN, striking a balance between performance and computational cost. Through extensive experiments across various image enhancement tasks, we consistently observe performance gains by integrating our TCN.
The contributions of this work are summarized as follows: 1) We present a novel perspective on image enhancement using a dedicated normalization technique. This technique enhances the learning of lightness adjustments by modeling consistent normalized features, while ensuring their complementarity for reconstructing the results. 2) We construct the caunitionnconstaan Normalization (TCN) by organizing normalization operations to satisfy the invertible mechanism, ensuring constant feature normalization and information transition. 3) Our proposed TCN compatible with existing enhancement architectures, allowing for convenient integration and performance improvement. Furthermore, we can derive multiple implementation formats for TCN and explore its applicability in various tasks, highlighting its potential for wide-ranging applications.
2
Related Work
Image enhancement tasks. Image enhancement tasks aim to improve the quality of low-visibility images by adjusting lightness and contrast components (e.g., illumination, color, and dynamic range).

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Recent years have witnessed rapid development in the related areas [1 12 13] For lightight image enhancement, algorithms are designed to enhance the visibility images captured under low-
light conditions [4] 15 16 17 18 19 7 20 21 22 23 In the exposure correction task, methods are
focused on correcting both underexposure and overexposure to normal exposure [5] 24 25 26 27]. For SDR2HDR translation, this task aims to design methods to convert images from a low-dynamic
range to a high-dynamic range [28] 29 30 31 32). While in image dehazing, this task requires
methods to enhance the contrast and recover the color shift problems [3 34
35 36 37). To this end,
image enhancement tasks cover variant scenes and remain challenges to be solved.
Normalization techniques. Normalization techniques have been studied for long time [8 39 40
41. Batch Normalization (BN) (38) normalizes the features along the batch dimension that stabilizes the optimization procedure. Instance Normalization (IN) [9] focuses on normalizing the instance-
statisties of features, which has been widely employed in style transfer tasks [42 43). Some other
variants of normalization, including Layer Normalization (I.N) [39]. Group Normalization (GN) [44]
and Position Normalization (PN) 45 have been proposed for facilitating the application of networks.
3 Method
In this section, we first briefly revisit the normalization techniques and then detail the design and mechanism of the proposed TCN. Finally, we present the variants of the TCN as implementation.
3.1 Preliminaries
Given a batch of features x E R NxCCHWW where N, C, H and W represent batch size, channel numbers, the spatial height and width, respectively. Let noij and eaoi denote pixel before and after normalization, where n e [1, NJ, E C,, E [1, H, [1, WJ. Without taking considering into affined parameters,
express normalization operation
Kncij = Norm(anaci)) Encij
+
where P and 0% denote the feature mean and standard deviation, is a small constant to preserve numerical stability. & {IN, BN, LN, GN) is to distinguish different normalization formats. Within the above normalization family, the calculation of μu and σ is different and are expressed as:
A
Σ
Eneij: Tk V [Al
Σ n.G.di
(Ineis px))
[4
1) IN: I INN =
[1, W]]:
2) BN: I IBN {(n,i,))m [1,N],i j [1. W1]:
4) GN: I& TaN =(GGújj)ee C/GC, [ WIW:
3) LN: I INN = (
[ []g E GJ,
where 4g is set of pixels, [/|] denotes the number of pixels and the group division.
Within deep neural networks, Eq. I is often affined with scaling and shifting parameters a and [::
Enciy = or Norm (Kncij) + + ===0 = a Eneis 14k + B.
Vo +
It is well-known that lightness can be considered as kind of style and Instance normalization can facilitate consistent style information 99] and image enhancement network optimization due to bridging the gap of different lightness representations.
Verifying normalization effect for lightness. Given an image v, and its lightness adjsssseddddstedddde version It, the relationship between F, and 2,, can be expressed in correction procedure [46 47 48 as:
Zb Az
where a linear transformation and y is for global non-linear adaption and close when content is not severely changed. Therefore, the p-norm distance between their normalized versions is:
[f((.)) f(xx)lpp = Ta - 14a AxT
14 1|p
Fa
1a
ANa - AWA lpp < ||.a - Az (5)
0a σb
Fa
Aoo
3

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Therefore, normalization reduces the distance of different lightness, which is also validated in Fig. [a). However, normalizing the statistics itself often blocks the information flow, which hinders the network from reconstructing the final results 49 and we describe it as follows.
Transitoon-nnoonstant of Normalization. Referring Norm(.) in the Eq. as its Jacobian matrix is expressed as:
2g
0k
Jz(z)= =
(6)
Oufp
Jafn
17 122
Lok
mxn
Therefore, the calculation of the above Jacobian matrix is det (Jy(x)) O, denoting the normalization operation is not invertible and resulting in works have demonstrated that IN would lead to severe information loss [42] and huge represen- tation ability changes [1) while LN and BN can keep almost all the original information representation ability. However, IN is more suitable than BN and LN for image enhancement tasks due to its strong capability of capturing and affecting style information, which is crucial for image enhancement. To this end, the main goal of this paper is introduce new mechanism that enables the IN can keep the information representation ability for image enhancement.
Meanwhile, in practice, previous
3.2 Transition-constal Normalization (TCN)
Based on the above analysis, we aim to refresh the normalization technique to enable it to transmit information constantly while normalizing the features. this end, we introduce the TCN as shown in Fig. [ which is free of parameters and is convenient to implement. Since IN can normalize different lightness effectively and thus is useful for image enhancement, we design the TCN based on the IN as its default implementation format in this paper.
Operation description. We construct the TCN by applying the normalization operations with a two streams flow design with subsampled features, where one stream provides the statistical information for normalizing another stream. Specifically, the feature F E BxxxxxXXW is firstly subsampled to F, E IR Bx4xCx it according to the unshuffle operation [52) as shown in Fig. (a), which is:
Fab Fl :., b. ::],
a, a {0,
where a and b denote the subsampling index. We divide Fg into two features F1 and F2 with two groups according to the sampling index (i,j) as:
Fi Concat(4"" FF1). F2 = Concat(F F!!)
(8)
where Concat denotes the concatenate operation along the channel dimension.
Then, we calculate the mean μlf and standard deviation 02 one stream feature F2 format, which are derived by setting I in Eq, 2 IIN:
1/
Σ
Faj.
Σ
(Fi, - 12)2
(9)
HW
HW
These statistics are utilized to normalize the feature Fi as the output in this stream:
F
F
(10)
+
Next, we subtract the feature E2 and f and obtain the output of another stream:
F p2 Vo
P = F2 -
(11)
Finally, the two stream features sampled with pixel shuffle operation to the original resolution with the shape of [N, Ç. H, Wj. They are further concatenated in the channel dimension as F e B BBxCXXxW which is the output of the TCN. This procedure is expressed as:
F Pinshafff((Connes((...]]
(12)

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where Pixshuffle(-) denotes the pixel shuffle operation [3]. We verify the above procedures satisfy the mansitiionconstan and normalization ability as below, respectively.
1) Verify the transition-constee ability. We validate the above two-stream design satisfies the invertible procedure and thus is transition-constant. To this end, Eq. [O]and Eq. 11 are re-written as:
F (Fi - M(()) S(F2),
(13)
F2 = F.2 Ri
where S()) and (-) denote standard deviation and mean functions, 2 denotes element division.
Inspired by the proof in RealNVP's [54] transformation, we need to calculate the Jacobian matrix of Eq. I3 (denote it as g). which is more intuitively written as:
F (Pi - M(E)) e S(F)),
(14)
A = F2 - F1 2 S(F2) + ((E) 2 S(F2),
We derive Jacobian matrix (detailed the supplementary) as:
aa
0f
8F1
0P
ast (2))
(15)
af
3F
DP;
Here, the above Jacobian matrix is further calculated as:
det(Jg) = aS(F2) #0
(16)
Upon the det(J,) f O, indicates that Jg is full rank, verifying the invertible property of TCN and further the transition-constant ability. To highlight, the TCN is an invertible function and would not block information flow, leading to the information transition constant for image reconstruction. We further present the relation of the TCN and the invertible operation more directly in the supplementary.
2) Verify the normalization ability. The normal- ization ability of the TCN is guaranteed by the pixel unshuffle operation in Eq.
Formats
Redefine 4. to Eg. 9 as
7 leading to the same statistics of Fab [55] 56]. Therefore, we have u2 R M1,02 R 01, and the Eq, i0 thus converted to:
TCN (IN)
TIN (default T; of Eq
TCN (BN)
IBN {(n,i,j)ln [I.
i (L, W]}
TCN (LN)
ILN = E(
i i [ IaN = ((c,
F
F
12
F
(17)
TCN (GN)
0 +6
Vo
+
E [g. C/Gl [1,HH, j W e (1
Therefore, it has the same format as Eq. D demon- strating that the operation in Eq, [0] has the normal- ization ability as the IN. Further, verify the above rules by the toy experiment (sce Sec. 4.I in Fig and Fig.
Table 1: The TCN family with different #2,02 calculation in Eq.
The core of the TCN is the statistic calculation manner of μ2z and 02 in Eq. which can be generalized in unified calculation manner in Eq, 2 and derive from other normalization formats of TCN, shown Table Note that, although GN and LN would less affect information representation ability (50), we experimentally find introducing the transition-constant design would improve their performance the supplementary. We provide more discussions in the supplementary.
Discussion.
3.3 Variants of TCN for Image Enhancement
Upon the above principles of TCN, we provide the following implementation variants within image enhancement task.
The original TCN. We construct the original TCN (see Fig. (a)) for image enhancement based on calculating statisties in Eq. of IN format, which is plug-and-play for networks.
The affined TCN. We extend the original TCN by introducing affined parameters and / to the normalization procedure, resulting in the affined TCN (Fig. (b)). We incorporate learnable shifting
5

---

Transtion-conttani Normulkation (TCN)
F2
F2
XCPs M2) BW
Fs
Un-shuffiee & Split
Tronsformation
Concatonate & Fixel Shuffie
Affined TCN
Skip
F
Ps
Fuo
TUN
F
P5
down
SestrableAAjue Affine tmmetrs
Fu
Figure 2: The illustration of the TCN operation and other TCN variants for image enhancement.
parameter & and scaling parameter y into μ22 and in Eq.
n/2 - μ12 + B, 0
02
(18)
where 1l and o represent the affined statistics. Then, we substitute Eq. to Eq.
F 102
Fi
P/
(19)
+e
Since e is a small constant near to O, the Eq.
19 can be approximated as:
F 2t22 3 Fi 1/2 F
-78
Vo + +
8r, B' Vol2 +
(20)
Vol +e
Eq 20 shares a format similar the affined normalization, with y and 3 as the learnable scaling
and shifting parameters in Eq.3 The affined TCN seamlessly integrates into image enhancement networks, serving plug-and-play solution. Notably, it maintains the information transition- constant property, as discussed in detail in the supplementary material.
The skip TCN. From Eq and IT the TCN generates two types of features: domain-invariant
lightness consistent feature F and domain-variant lightness inconsistent feature Fz. Previous studies [57 58) have demonstrated the effectiveness of incorporating the domain-varte variant component into deep encoder-decoder networks while skipping the inmaiiannvariint component the decoder layer. In this work, we propose the skip TCN architecture, illustrated in Fig. (c).
Given feature F in an encoder layer, we convey its lightness inconsistent feature F2, obtained from Eq. 10 to the downsampled deeper layer that derives Fa using the following expression:
Fa = Down, (F) + Fz.
(21)
where Downg means downsampling with a factor of 2. While for the lightness consistent feature F derived in Eq. I we skip it to the corresponding decoder layer feature F, with the statistic μl and 02 derived in Eq. y We integrate them by inverting the operation of Eq. 10 and Eq. I
Fw1 = 02 + Fuz = Fu, F.. UP,((F.)) + Pisshuff(Concaa(F.., Fu2)),
(22)

---

Skip TCV
Here, the upsampling operation with a factor of 2 is de- noted Up2, and Fuo represents the skip TCN result. The skip TCN prioritizes the processing of the lightness component while preserving lightness invariant features, mitigating learning difficulties. Further discussion is pro- vided in the supplementary material.
7DV
Corn
Skip
Affindd TCh
Construct a very efficient TCN-based Network. We introduce TCN-Net, an efficient network architecture de- picted in Fig. 3 which combines affined TCN and skip TCN. This framework adopts an dendder-deoder-baare architecture with vanilla convolution blocks to depict the effectiveness of the TCN. Further details and discussions are available the supplementary material.
Skip
Figure 3: The overview of the TCN-Net.
Experiments
In this section, we validate the effectiveness and scalability of our proposed TCN on various image enhancement tasks. We provide more experimental results in the supplementary material.
None
IN
TCN
Respectively Select
55
54.61
50.53
50
Optimization
45
40
Encder
Decoder
Feahures
Insert
30
29.58
Loss
25
None
TCN
Figure 4: Toy experiment of self-reconstruction. The left is the setting of toy experiments with inserting different operations, and the right presents the self-reconstrruction PSNR of testing images.
t-SNE
of Underexposure
of Underexposure Overexposure F; Overexposure
Non Mon-Clin -lusteeee
Underexposure Input
Underexposure's
Underexposure's F
Clustered
Overexposure Input
Overexposure's
Overexposure's F;
Figure 5: Feature visualization of toy experiment. In left and right parts, show the feature in the TCN of underexposure and overexposure samples when testing them with inserting TCN in Fig.
4.1 Toy Experiment
To illustrate the proposed TCN has the ability normalize the features while transition-constann. introduce a toy experiment as shown in Fig. we construct an encoder-deccoder-base architecture for reconstructing the input image, where the TCN and other normalization formats inserted between the encoder and decoder as different versions. Then we train this seff-reconstruction architecture on 1000 samples from MIT-FiveK dataset [59] until its convergence and the self relfsseconttt effect on another 100 samples from the same dataset. The quantitative results in the right of Fig. 4 indicate that our TCN reconstructs the input image better than directly inserting IN, demonstrating the information
property. Furthermore, we test 100 underexposure samples and 100 overexposure samples from the SICE dataset, and we provide the feature distribution F and 9 (input and normalized output of the TCN) in the left part of Fig. as well as feature maps in the right part of Fig. 5 The different exposure features processed by the TCN get to be

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Settings
#Param
Flops (G)
LOL
Huawei
FiveK
DRBN (Baseline) 15
0.53M
39.71
19.95/0.7712
20.64/0.61.6
22. 1/0.8684
+IN
0.53M
39.71
20.73/0.7986
21.01/0.6200
22.93/0.8727
+Original TCN
0.53M (+0)
39.71 (+0)
21.15/0.8190
21.12/0.6242
23.98/0.8851
+Affined TCN
0.53M (+0)
39.71 (+0)
21.29/0.8167
21.04/0.6231
23.92/0.8858
+Skip TCN
0.53M (+0)
39.77 (+0.07)
21.52/0.8271
21.15/0.6195
23.82/0.8833
SID (Baseline) 14
7.40M
51.06
20.85/0.7845
19.68/0.6050
21.49/0.842 8425
+IN
7.40M
51.06
20.51/0.7858
20.09/0.6034
21.75/0.C 8453
+Original TCN
7.40M (+0)
51.06 (+0)
21.43/0.7913
20.53/0.6065
23.11/0.88 1581
+Affined TCN
7.40M (+0)
51.06 (+0)
21.35/0.7866
20.62/0.6077
23.20/0.866 8624
+Skip TCN
7.4IM (+0.01)
51.42 (+0.36)
21.92/0.8056
20.76/0.6083
23.61/0.8704
TCN-Net
0.012M
0.97
22.08/0.7895
20.99/0.6121 23.47/0.8663
Table 2: Comparison over low-light image enhancement in terms of PSNR/MS-SSIM.
Settings
MSFC
SICE
DRBN (Baseline) 15
19.52/0.8309
17.65/0.6798
Training on SICE dataset
+IN
21.98/0.8463
20.15/0.6941
+Original TCN
22.37/0.8533
20.74/0.7133
+Affined TCN
22.41/0.8504
20.85/0.7192
PSNR(dB) (d))
+Skip TCN
22,48/0.8572
20.65/0.7159
SID (Baseline) [14
19.04/0.8077
18, 15/0.66650
10
+IN
21.36/0.8373
19.81/0.6667
SID+IN SID+TCN
+Original TCN
22.31/0.8522
10.51/0.6745
+Affined TCN
22.43/0.8542
20.68/0.6757
SID IC+AAfiind--TN
SkD+
+Skip TCN
22.36/0.8603
20.64/0.6855
TCN-Net
22.19/0.8480
20.72/0.7024
20005
12000
03200
0000:
100030
320000 340000
Iterations
Table 3: Comparison over exposure correction.
Figure 6: Training PSNR on exposure correction.
clustered, demonstrating the normalization ability the TCN for extracting the lightness-consittenn representation of different samples. We provide more discussions in the supplementary material.
4.2 Experimental Settings
Low-light Image Enhancement. Following previous works 60
61 we employ three widely used datasets for evaluation, including LOL dataset [7], Huawei dataset [60] and MIT-FiveK dataset [59].
We employ two different image enhancement networks, DRBN [15 and SID [4 as baselines.
Exposure Correction. Following 622
adopt MSEC dataset
2 and SICE dataset 663 for
evaluations. The above two architectures, i.e., DRBN 15 and SID are regarded as baselines.
SDR2HDR Translation. Following 30 choose the SRITM dataset 31 and HDRTV dataset 30 for evaluation. We employ the structures NAFNet [64] with its three basic units as the bascline in the experiments.
Image Dehazing. Following 33), we employ the RESIDE dataset (65 consisting of Indoor and Outdoor parts for evaluations. We adopt the network of PFFNet 666 as the baseline for validation.
4.3 Implementation Details
Since there exist three TCN formats in Sec. B.3 we respectively integrate them into the baseline to conduct experiments. For comparison, we perform the experiments baseline networks and the integration of the IN operation. Additionally, the TCN-Net in Sec. 3.3 is also performed in experiments. We train all baselines and their integrated formats following original settings, and our TCN-Net until it converges. More implementation details are provided in the supplementary.

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Settings
SRITM
HDRTV
Settings
Indoor
Outdoor
NAFNet (Baseline) 664
33,44/0.9537
36,49/0.9706
PFFNet (Bascline) [66
21.74/0.8452
24.47/0.9274
33.62/0.9491
36.62/0.9683
+IN
23. 23.13/0.8583
25.61/0.9309
+Original TCN
33.6910.9505
36,94/0.9712
+TCN
23.57/0.8633
25.63/0.331
Affined TCN
33.65/0.9499
36.55/0,9716
+Aflined TCN
23.71/0.8652
25.84/0.9312
+Skip TCN
33.51/0.9513
36.64/0.9720
+Skip TCN
23.21/0.8708
25.63/0.9315
TCN-Net
32.48/0.9439
36.78/0.9744
TCN-Net
24.06/0.8645
23.72/0.8572
Table 4: Comparison SDR2HDR translation.
Table Comparison over image dehazing.
Ground truth Figure 7: The visual comparison of low-light image enhancement on the MIT-FiveK dataset.
Input
DRBN
DRBN+TCN
4.4 Comparison and Analysis
Quantitative Comparison. The model comparisons are conducted over different configurations, as illustrated in the implementation details. We present the quantitative results from Table to Table where the best and second-best best results are highlighted in bold and underlined. As can be seen, almost all formats of the TCN that we incorporate have improved the performance across the datasets in all tasks, validating the effectiveness of our method. Specifically, integrating variants of TCN helps improve the training performance of baseline as shown in Fig. 6 In contrast, naively integrating the IN could not always bring performance improvement (i.e., the results of SID in Table ().. All the above results suggest the effectiveness of our proposed method without introducing any parameters. Moreover, the proposed TCN-Net achieves effective performance with efficiency. All the above evaluations prove the convenience of applying the TCN in image enhancement tasks.
Qualitative Comparison. We report the visual results of low-light image enhancement the MIT-FiveK dataset (59) due to the limited space. As shown in Fig. 7 the integration of the TCN leads to more visually pleasing effect with less lightness and color shift problems compared with the original baseline. We provide more visual results in the supplementary material.
4.5 Extensive Applications
The TCN can also be applied other machine vision tasks that demonstrate its extensibility. Since TCN is proposed to extract lightness (a kind of style) invariant feature while keeping information transition-constant
introduce another two tasks that also related to style information, including pan-sharpening and medical segmentation. For pan-sharpening, it aims to fuse two style images, and we hope TCN can extract their invariant information with information preserving; For medical segmentation, there often exists style domain gap between training and testing sets.
Extension medical segmentation. We apply the TCN on the UNet 677 and AutNee 668 in the medical segmentation task. We train the baseline and its integrated version on the heart segmentation task of Medical Segmentation Decathlon challenge dataset [69]. As shown in Table 6 our TCN improves and keeps the performance of U-Net and Att- Unet, respectively, while IN brings a significant performance drop. The results suggest the scalability of the TCN compared with the IN.
Extension on pan-sharpening. We apply original TCN to the GPPNN 70 and PANNet
baselines in the pan-sharpening task, which is common task in guided image super-resolution. We integrate when extracting pan and multi-spectral features, and experimental results on WorldView II dataset 722 73 in Fig. suggest the effectiivnees of the TCN.

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Settings
Dice t
HID95,
42.00
beseline
UNet(baseline)
0.9162
3,9188
41.75
baseline TCN
baseling+1t
UNet(+IN)
0.9171
7.7305
41.25
41.11
UNe((+TCN)
0.9204
4.0171
41.00
40.67
AItUNc(basslline)
0.9182
3,5453
40.75
40.50
AIUNNe(++IN)
0.9193
6.8549
40.25
AlUNe((+TCN)
0.9180
3.6241
40.00
GPANN Comparison over pan-sharpening for GPPNN and PANNet.
Approeches/Wortevieewz
Table 6: Comparison over medical seg- mentation in terms of dice and HD95.
Figure 8:
5 Limitation and Discussion
Firstly, we validate the effectiveness of TCN in image enhancement tasks, while the investigation of applying TCN other image restoration tasks will be explored in the future, such as the all- in-one image restoration task that meets similar challenges like image enhancement tasks, which has been pointed in some related works [74 75). Second, dedicated to image enhancement tasks, we mainly discuss the IN format of the TCN. However, other normalization formats can be future explored for other tasks. Moreover, the design formats of the TCN could inspire some areas that also require cunsition-constan such as image fusion tasks [76]. Finally, the TCN could introduce very few computation burdens although free of parameters, which negligible compared with its bring performance improvement. Note that the focus of this work is beyond introducing plug- and-play operation existing networks for performance gain. The introduced TCN can new choice of normalization and feature disentanglement, which excavate consistent representations while preserving information when developing new model that requires this property.
Conclusion
In this paper, we introduce perspective that develops the normalization technique tailored for image enhancement approaches. We propose the TCN that transits the information constantly with the invertible constraint, meanwhile, keeps the normalization ability for capturing lightness consistence representations. The proposed TCN is general operation that can be integrated into existing networks without introducing parameters. Extensive experiments demonstrate the effectiveness and scalability of applying the TCN and its variants in various image enhancement tasks.
Broader Impact
Image enhancement is an important task that improves the quality of these images, exhibiting high value of research and application. Our method introduces normalization operation with information transition-constant property, which shows promising results that improve the learning ability of networks for image enhancement tasks conveniently. However, there could be negative effects brought by the proposed methodology. For example, some people may prefer the image with dim light effect, which would be eliminated by the image enhancement algorithm. In these cases, it suggested to combine the users' preferences to achieve customized image enhancement effects.
Acknowledgements.
This work supported by the JKW Research Funds under Grant 20-163-14-12-011- and the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.
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References
[1] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transactions on Image Process- ing, 27(6):2828-244) 2018.
[2] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982-995 2016.
[3] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2782-278227 2790, 2016.
[4] Bolun Cai, Xianming Xu, Kailing Guo, Kui Jia, Bin Hu, and Dacheng Tao. A joint intrinsic- extrinsic prior model for Retinex. In Proceedings of the IEEE International Conference on Computer Vision, pages 4000-4009, 2017.
[5] Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, and John Paisley. A fusion-based enhancing method for weakly illuminated images. Signal Processing. 129:82-96. 2016.
[6] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. LLNet: A deep autoencoder approach to natural low-light image enhancement. Puttern Recognition, 61 61:00666,, 2017.
[7] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep Retinex decomposition for low-light enhancement. arXiv preprint arXiv: 0888.00560 2018.
[8] Jiaying Liu, Dejia Xu, Wenhan Yang, Minhao Fan, and Haofeng Huang. Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision. 129: 153-1184, 04 2021.
[9] Dmitry Ulyanov, Andrea Vedaldi. and Victor Lempitsky. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 2016.
[10] Jie Huang, Zhiwei Xiong, Xueyang Fu, Dong Liu, and Zheng-Jun Zha. Hybrid image enhance- ment with progressive laplacian enhancing unit. ACM MM, pages 1614-1622, 2019.
(11) Chongyi Li, Chunle Guo, Ruicheng Feng, Shangchen Zhou, and Chen Change Loy. Cudi: Curve distillation for efficient and controllable exposure adjustment. arXiv preprint arXiv:2207. 14273, 2022.
[12] Naishan Zheng, Jie Huang, Man Zhou, Zizheng Yang. Qi Zhu, and Feng Zhao. Learning semantic degradation-awan guidance for recognition-driven unsupervised low-light image enhancement. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3678-
3686, 2023.
[13] Man Zhou, Jie Huang, Ch Guo, and Chongyi Li. Fourmer: An efficient global modeling paradigm for image restoration. In International Conference Machine Learning, pages 42589-42601. PMLR, 2023.
[14] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
[15] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to percep- tual quality: A supi-super approach for low-light image enhancement. In Proceedings of the IEEE/CVF international conference on computer vision (CVPR), pages 3063-3077 2020.
[16] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex- net: Retinex-basse based deep unfolding network for low-light image enhancement. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5901-5910, June 2022.

---

[17] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the ACM International Conference on Multimedia, pages 1632-1640, 2019.
[18] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. International Journal of Computer Vision, (29(4):1013-1)) 2021.
[19] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1780-1789, 2020.
[20] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration underexposed images via robust Retinex decomposition. In IEEE International Conference Multimedia and Expo, pages 1-6, 2020.
[21] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pages 6849-6857, 2019.
[22] Naishan Zheng, Jie Huang, Qi Zhu, Man Zhou, Feng Zhao, and Zheng-Jun Jun Zha. Enhancement by your aesthetic: An intelligible unsupervised personalized enhancer for low-light images. In Proceedings of the 30th ACM International Conference Multimedia, pages 6521-6529, 2022.
[23] Naishan Zheng, Man Zhou, Yanmeng Dong, Xiangyu Rui, Jie Huang, Chongyi Li, and Feng Zhao. Empowering low-light image enhancer through customized learnable priors. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12559-
12569, October 2023.
[24] Mahmoud Afifi, Konstantinos G Derpanis, Bjorn Ommer, and Michael Brown. Learning multi- scale photo exposure correction. In Proceedings of the IEEE/CVF Conference Computer Vision and Patlern Recognition, pages 9157-9167, 2021.
[25] Haoyuan Wang, Ke Xu, and Rynson W.H. Lau. Local color distributions prior for image enhancement. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
[26] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial frequency interaction. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
[27] Jie Huang, Feng Zhao, Man Zhou, Jie Xiao, Naishan Zheng, Kaiwen Zheng, and Zhiwei Xiong. Learning sample relationship for exposure correction. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9904-9913, June 2023.
[28] Xiangyu Chen, Yihao Liu, Zhengwen Zhang, Yu Qiao, and Chao Dong. HDRUnet: Single image hdr reconstruction with denoising and dequantization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 354-3634 2021.
[29] Xuan Dong, Xiaoyan Hu, Weixin Li, Xiaojie Wang, and Yunhong Wang. Miehdr enn: Main image enhancement based ghost-free high dynamic range imaging using dual-lens systems. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1264 1264-1272. 2021.
[30] Xiangyu Chen, Zhengwen Zhang, Jimmy S Ren, Lynhoo Tian, Yu Qiao, and Chao Dong. A new journey from sdrtv to hdrtv. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4500-4509 2021.
[31] Soo Ye Kim, Jihyong Oh, and Munchurl Kim. Deep sr-itm: Joint learning of super-resolution and inverse tope-mapping for 4k uhd hdr applications. In Proceedings of the IEEE/CVF International Conference on Computer Vision pages 3116-3125, 2019.
[32] Jingwen He, Yihao Liu, Yu Qiao, and Chao Dong. Conditional sequential modulation for efficient global image retouching. In European Conference on Computer Vision, pages 679-695. Springer, 2020.
12

---

[33] Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuae Yang. Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEF/CVF international conference on computer vision, pages 2154-2164, 2020.
[34] Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. Aod-net: All-in-one 2017.
dehazing network. In ICCV, pages
[35] Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang. Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10551-10560, 2021.
[36] Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. Griddehazenet: Attention- based multi- scale network for image dehazing. Proceedings of the IEEE/CVF international conference on computer vision, pages 7314-730 7323, 2019.
[37] Xiang Chen, Zhentao Fan, Pengpeng Li, Longgang Dai, Caihua Kong, Zhuoran Zheng, Yufeng Huang, and Yufeng Li. Unpaired deep image dehazing using contrastive disentanglement learning. In European Conference on Computer Vision, pages 632-648. Springer, 2022.
[38] Sergey loffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448 456. PMLR, 2015.
[39] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization, arXiv preprint arXiv:1607.064500 2016.
[40] Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin. Understanding and improving layer normalization. Advances in Neural Information Processing Systems, 32, 2019.
[41] Zachary Nado, Shreyas Padhy, D Sculley, Alexander D' Amour, Balaji Lakshminarayanan, and Jasper Snoek. Evaluating prediction cidticttion-imeeeeee batch normalization for robustness under covariate shift. arXiv preprint arXiv:2006. 10963, 2020.
[42] Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE international conference on computer vision, pages 1501-1510, 2017.
[43] Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, and Shilei Wen. Dynamic instance normalization for arbitrary style transfer. In Proceedings of the AAAI Conference Artificial Intelligence, 2020.
[44] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[45] Boyi Li, Felix Wu, Kilian Q Weinberger, and Serge Belongie. Positional normalization. In Advances in Neural Information Processing Systems, 2019.
[46] Francesco Banterle, Alessandro Artusi, Kurt Debattista, and Alan Chalmers. Advanced high dynamic range imaging. CRC press, 2017.
[47] Steve Mann and Rosalind W. Picard. Being 'undigital' with digital cameras: extending dynamic range by combining differently exposed pictures. 1994.
[48] Gabriel Filertsen, Rafal Konrad Mantiuk, and Jonas Unger. A comparative review of tone- mapping algorithms for high dynamic range video. In Computer graphics forum, volume 36, pages 565-592. Wiley Online Library, 2017.
[49] Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, and Li Zhang. Style normalization and restitu- tion for generalizable person re-identification. Proceedings of the IEEE/CVF international conference on computer vision, pages 3140-3149, June 2020.
[50] Antoine Labatie, Dominic Masters, Zach Rason-Rosen, and Carlo Luschi. Proxy-normalizing activations to match batch normalization while removing batch dependence. Advances in Neural Information Processing Systems, 14:16990-77006, 2021.
13

---

[51] Ekdeep S Lubana. Robert Dick, and Hidenori Tanaka. Beyond batchnorm: towards unified understanding of normalization in deep learning. Advances in Neural Information Processing Systems, 34:4778-44777 2021.
[52] Bin Sun, Yulun Zhang, Songyao Jiang, and Yun Fu. Hybrid pixel-unshuffeed network for lightweight image super-resolution. arXiv preprint arXiv:2203.0892) 2022.
[53] Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop. Daniel Rueckert, and Zehan Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1874-1883, 2016.
[54] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. International Conference on Learning Representations, 2017.
[55] Yuqian Zhou, Jianbo Jiao, Haibin Huang. Yang Wang, Jue Wang, Honghui Shi, and Thomas Huang. When awgn-based denoiser meets real noises. Proceedings of the AAAI Conference on Artificial Intelligence, 2019.
(56) Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee. Ap-hsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[57] Mengping Yang, Zhe Wang, Ziqiu Chi, and Wenyi Feng. Wavegan: Frequency-aware gan for high-fideliyy few-shot image generation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 1-17, 2022.
[58] Jie Liang. Hui Zeng, and Lei Zhang. High-resolution photorealistic image translation in real- time: A laplacian pyramid translation network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9392-9400) 2021.
[59] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tomal adjustment with a database of input/output image pairs. In Proceedings of the IEEE/CVF international conference on computer vision, pages 97-104, 2011.
[60] Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han. R2rnet: Low- Log-light image enhancement via real-low to real-normal network. arXiv preprint arXiv:2106. 14501, 2021.
[61] Lin Zhao, Shao-Ping Lu, Tao Chen, Zhenglu Yang, and Ariel Shamir. Deep symmetric network for underexposed image enhancement with recurrent attentional learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12075-12084, 2021.
[62] Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao and Zhiwei Xiong. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the IEEF/CVF Conference Computer Vision and Patlern Recognition, pages 6043-6052. 2022.
[63] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning deep single image contrast enhancer from multi-exposure images. IEEE Trunsactions on Image Processing, 27(4): 2018
[64] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In Proceedings of the European Conference on Computer Vision (ECCV), pages 17-33. Springer, 2022.
[65] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1):492-505, 2019.
[66] Kangfu Mei, Aiwen Jiang, Juncheng Li, and Mingwen Wang. Progressive feature fusion network for realistie image dehazing. Asian Conference on Computer Vision (ACCV), pages 203-215. Springer, 2019.
14

---

[67] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: for biomedical image segmentation. Medical Image Computing and Computer-Assisted InterventionMMICCA. 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 231-241. Springer, 2015.
Convolutional networks
[68] Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazumari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al. Attention u-net: Learning where to look for the panereas. arXiv preprint arXiv:1804.03380 2018.
[69] Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett Landman, Geert Litjens, Bjoern Menze, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv: 0900,, 2019.
[70] Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, and Chunxia Zhang. Deep gradient projection networks for pan-sharpening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1366-1375, June 2021.
[71] Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, and John Paisley. Pannet: A deep network architecture for pan-sharpening. In IEEE International Conference on Computer Vision, pages 4149-54557 2017.
[72] Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, and Feng Zhao. Mutual information- driven pan-sharpening. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pages 1798-1808, June 2022.
[73] Man Zhou, Jie Huang, Keyu Yan, Hu Yu, Xueyang Fu, Aiping Liu, Xian Wei, and Feng Zhao Opatial-frequency domain information integration for pan-sharpening. In Computer Vision- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVIII, pages 274 291. Springer, 2022.
[74] Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng. All-in-one image restoration for unknown corruption. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17452-17462, 2022.
[75] Wenxin Wang, Boyun Li. Yuanbiao Gou, Peng Hu, and Xi Peng. Relationship quantification of image degradations. arXiv preprint arXiv:22 0418 2022.
[76] Jinyuan Liu, Xin Fan, Zhanbo Huang, Guanyao Wu, Risheng Liu, Wei Zhong, and Zhongxuan Luo. Target-aware dual adversarial learning and multi muutii-ceenario multi-modality benchmark to fuse infrared and visible for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5802-5811, 2022.
15


Reviews:
[{"review_id": "IANPsytwGc", "reviewer": "Reviewer_LSyJ", "summary": "1. A new normalization technique is introduced that transits information constantly for image enhancement.\n2. The introduced TCN covers a range of formats and is compatible with existing method.\n3. The introduced technique exhibits extensibility to various scenes and tasks through experiments.", "questions": "Why the extensive experiments are performed in guided image super-resolution task and medical segmentation task? What about other tasks? Besides, what makes this technique is able to be extended to other tasks? What makes authors consider to do this thing? Furthermore, Why not incorporate more kinds of models (CNN or transformer) as baselines?", "limitations": "The limitations and potential negative societal impact of their work have been addressed in this paper.", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "It is a novel idea that normalization is improved to meet transition-constant goal with following points:\n+ Its design is easy to understand and reasonable\n+ Its design demonstrates extensibility in terms of both implementation manners and applicable tasks\n+ writing is clear to follow", "weaknesses": "However, there remains issues to be dealt with：\n- It claims that information transit ability is crucial for image enhancement, why hasn't this ability been incorporated into other operations such as CNN and transformers? \n- Its motivation may not be directly correlated with image enhancement, how information transit-inconstant is not supportive for image enhancement? CNN and transformer are not transition-constant and they are commonly used and have proven effective.\n- Its built manner is limited in few cases, when for implementation, small resolution feature may not be operated with resolution need to be further reduced.\n- It lacks of visual comparison and deep analysis about ablation studies, merely providing quantitative results could not be very supportive.\n- Its toy experiments may not be supportive. When for self-reconstruction, TCN still has obvious lower performance than with “None”. \n- Its design is similar with Half-instance normalization, yet the performance is not significantly higher than Half-instance normalization in ablation studies.\n- It contains writing errors, inaccurate equation denotations and figure denotations. Please check and rectify them carefully.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and efforts and here we present the rebuttal.\n\n**1. About the relationship of transition-constant ability and image enhancement.**\n\nFirst, for architectures such as CNN and transformer, they convey information with the driven of reconstruction losses, thus the whole part of them is nearly to transition-constant. However, for Instance normalization (IN), it discards information [1] instead of learning to filter the information like CNN or transformer, making the sub-sequential parts cannot restore the information. This is how IN’s weakness is different from other architectures. Second, due to the IN’s weakness, and its advantage is normalizing different lightness and thus is useful for image enhancement tasks, we improve the IN with the transition-constant ability that enables IN can be practically used for image enhancement. Note that this ability is equipped for IN instead of other parts. Finally, DSN [2] shows the transition-constant ability of networks can be developed for image enhancement, while this work doesn’t explore IN’s property. We will add the above discussion in the revision.\n\n[1] Jin et al. Style normalization and restitution for generalizable person re-identification. CVPR 2020.\n\n[2] Zhao et al. Deep symmetric network for underexposed image enhancement with recurrent attentional learning. ICCV 2021.\n\n**2. About applying TCN in small features.**\n   \n   We implement TCN on the feature with large size features, while small size features contain much less information than the large size, therefore applying the TCN in such information-reduced feature is not necessary. We will further clarify this point in the revision.    \n   \n**3. About the demonstration of ablation studies.**\n\nFirstly, we need to emphasize that the comparison results of baseline and with IN itself can be the ablation, therefore, the number of results is enough. Besides the ablation studies in Table 8 of the supplementary, other parts can also depict this role. The analysis of toy experiments in the supplementary verifies the reasonableness of our TCN’s design, which corresponds to the results of Table 8. Due to limited space, we will supplement more analysis and visual results in the revision.\n\n**4. About the toy experiment.**\n\nWe conduct the toy experiment to show that the TCN has better performance than IN for conveying information. In fact, as depicted in Fig.4, inserting TCN for self-reconstruction has much higher performance than inserting IN, and the result of “None” is a reference to measure how the TCN is effective for conveying information (TCN is only 4.08 dB lower than “None”, while 21.95 dB higher than IN, note that 4dB is not a large number in 50+dB level). Therefore, the results of toy experiments can demonstrate better transition-constant properties of TCN than IN. Besides, the toy experiment also depicts the normalization ability of TCN.\n\n**5. About the half-instance normalization.**\n      \nFirstly, our TCN outperforms it by about 0.2dB, which is considerable.  Then, half-instance normalization is an ablation module without theocratic design, while the proposed TCN is designed with strong motivation based on the transition-constant constraint, which is a supplement of half-instance normalization. Notably, our method would inspire more work toward designing normalization formats that is suitable for image enhancement tasks and other image processing tasks, which have not been mentioned or explored before.    \n\n**6. Some suggestions about writing.**\n\nThanks for your kind reminder, we will revise our paper carefully in the revision.\n\n\n**7. About extensive experiments.**\n\n**(1)** The reasons why we add the extensive tasks: Since TCN is proposed to extract lightness (a kind of style) invariant feature while keeping information transition-constant, we introduce the extensive tasks to verify this property can benefit more tasks that are similar to image enhancement, where these tasks are related to style information processing, thus proving the TCN’s extensive ability.\n**(2)** The reasons why we choose these two tasks: These two tasks are relevant to the style of information processing and thus can evaluate the TCN’s extensibility. For pan-sharpening, it aims to fuse two modality information of different style, and we hope TCN can extract their invariant information with information preserving; For medical segmentation, there often exist a domain gap of training and testing, and we hope TCN’s robust feature extracting ability can improve the testing accuracy that could be influenced by the domain gap. Moreover, we have conducted these two tasks before, and some other tasks can also be explored with TCN. \n**(3)** Discussion about more tasks: Yes. We believe that the TCN can also be extended to other tasks about style processing, such as domain adaptation and multi-sensor image fusion. The TCN could extract the invariant component of different style information effectively that elevate the performance.\n**(4)** Lastly, we have presented CNN- and transformer-based backbone with TCN in the supplementary materials, proving the effectiveness of the TCN. We will add the above information in the revision properly."}, {"review_id": "rYRAbhcPNF", "reviewer": "Reviewer_HV1b", "summary": "In this paper, a transition-constant normalization (TCN) is introduced to harness the potential of applying normalization techniques for image enhancement. The TCN is designed with two rules, adhering to the principle of invertible information transmission and dividing the features into two streams with consistent statistical properties. Notably, the TCN is parameter-free and offers multiple usage formats, enabling its integration into existing methods and the creation of new structures. To showcase the benefits of this approach, extensive experiments are conducted across diverse image enhancement tasks.", "questions": "If the feature resolution cannot be divided evenly by 2, it may affect the implementation of the TCN deepened on pixel shuffle operation. The pixel shuffle operation typically relies on dividing the feature maps into subgroups and rearranging them to increase the resolution. If the feature resolution is not divisible by 2, it may lead to incomplete subgroups or uneven rearrangement, potentially causing issues in the TCN's implementation. In such a case, the results of the TCN deepened on pixel shuffle operation could be compromised. The network may encounter difficulties in accurately reconstructing images or extracting meaningful features due to the incomplete or uneven reshuffling of feature maps. It is important to consider the compatibility between the feature resolution and the operations used in the TCN architecture to ensure optimal results.", "limitations": "Yes. Limitations are adequately addressed.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "This paper examines the characteristic of normalization and presents its core design approach: \"normalizing partial representations to ensure consistent learning while preserving constant information for image reconstruction.\" Overall, the paper's architecture is clear, and the writing is well-done. The experiments conducted are sufficient. One particularly interesting aspect highlighted in this study is the notion that the introduced TCN serves as a concept rather than a specific operation. The summarized variations of different TCN formats are valuable and open up opportunities for future exploration.", "weaknesses": "(a) The characteristic of TCN, which only normalizes partial features, appears to conflict with the motivation. I am concerned that this characteristic might limit its ability. Can you clarify whether this limitation exists? \n\n(b) Layer normalization is widely utilized in transformer structures. Therefore, the paper should include a more comprehensive description and discussion of this aspect.\n\n(c) I am curious why the calculation in Equation 9 is performed in the spatial dimension, similar to IN (Instance Normalization). \n\n(d) It appears that the methods included for TCN are not novel. For instance, why weren't more recent methods like Restormer [1] included in the study? \n\n(e) In the supplementary material provided, it is stated that \"the TCN can be constructed in other formats if the invertible rules can be satisfied.\" It would be beneficial if the paper could provide more detailed information about these alternative formats. \n\nAdditional problems:\n\n(a) The introduction and related works sections fail to address invertible techniques, which is a significant oversight. \n\n(b) There are some typos in the paper, such as \"other other\" in line 155, which should be corrected to \"other.\" \n\n(c) In Figure 2, there is a typo in the calculation of \\hat{F_1}. Additionally, the symbol ε is missing from the equation. \n\n(d) The detailed architecture of TCN-Net is not provided in the paper, and it should be included for clarity. \n\n(e) The supplementary material mentions GN, but it does not specify the settings or parameters used for GN. More information about the GN setup should be provided.\n\n[1] SW Zamir. Efficient Transformer for High-Resolution Image Restoration. CVPR 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and efforts and here we present the rebuttal.\n\n**1. Would the TCN’s ability limited?**\n\nThe answer is no and here are the reasons: Firstly, the partial normalized features are integrated into the original one evenly in each channel, and thus each channel feature of the TCN’s output is more closer to the normalized feature overall, demonstrating the normalization ability is not limited. Secondly, the toy experiments depict that the TCN can normalize different lightness effectively, meanwhile it can convey information constantly effectively with well self-reconstruct ability. Therefore, the TCN’s ability is not limited both theocratically and experimentally.\n\n**2. More discussion about LayerNorm.**\n\nThanks for the suggestion. In fact, LayerNorm(LN) plays the role of balance activation function in the Transformer, which is different from what we explore for image enhancement. Besides, LN has a weaker activation ability to the feature than IN, thus improving IN with TCN formats is more meaningful to extend it for normalizing lightness while conveying the information constantly. Finally, the experiments in the supplementary (Table 2) also validates that equipping LN with TCN format can improve performance, and TCN can also improve the performance of NAFNet with LN.   \n\n**3. About the calculation in Eq. 9**\n\nThis calculation is defined based on IN calculation for the mean and variance, since IN has the best performance for activating features [1] and is often employed for style transfer that is related to image enhancement. Thus, we employ IN for implementing the default TCN format. \n\n[1] Antoine et al. Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence. NeurIPS 2021.\n\n**4. About performing experiments in recent methods?**\n\nThanks for your suggestion. We have included a more recent architecture NAFNet in the supplementary (Table 2). Due to the time limit, we also present the results of Restormer as you suggested on the LOL dataset in low-light image enhancement here. Note that the core of our method is to enable existing networks can be improved with the proposed TCN (instead of SOTA), which can also help construct lightweight backbones like the proposed TCN-Net.\n| Model | Methods |PSNR | SSIM |\n|----|----|----|----|\n| Restormer| Original | 22.24 |0.828|\n|        |+IN| 22.06|0.824|\n|        |+TCN | 22.47|0.831|\n\n**5.More information about some technique details in the supplementary.**\n\nThanks for the suggestion, we describe these details as follows:\n\n(1) TCN can be constructed under other invertible formats. For example, the IN format can be designed following the invertible constraint in [2], which is different from the current format. We will further discuss them in the revision. (2) We have already presented the TCN’s details in supplementary(Sec. 2), we will add a detailed figure to describe it. (3) We set the GN implementation as follows: the group size is set as 4, and each group has the same number of channels. We will add this part in the revision.\n\n[2] Lynton et al. Conditional Invertible Neural Networks for Guided Image Generation. ICLR 2020.\n\n**6. About the discussion of related works and some writing typos.**\n\n  Thanks for your kind reminder. We will add the discussion about invertible techniques, and correct the writing typos correctly in the updated version.\n\n**7.About applying TCN for features with odd number resolution.**\n\n  In experiments, most images’ resolution can be divided by 2, and we usually apply TCN in the large features that their sizes can be divided by 2. When applying TCN on features with odd number resolution, we need to resize the feature to the nearest even resolution preliminary. We will add this technique detail in the updated version."}, {"review_id": "il13JJd9rE", "reviewer": "Reviewer_uwKS", "summary": "This paper proposes an invertible feature normalization module based on the design of coupling layers in flow-based models [1]. The authors experimentally evaluate the proposed model on various image enhancement tasks such as low-light image enhancement, HDR, image dehazing, as well as medical segmentation and guided image super-resolution as downstream tasks. The results show improvements over their baseline methods.\n\n[1] Density estimation using Real NVP, ICLR 2017.", "questions": "Please see the Weaknesses.", "limitations": "This paper has no apparent negative societal impact.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper covers a range of tasks in the experiments. The module design is simple and achieves comparable speed to previous feature normalization methods.", "weaknesses": "(1) If I understand correctly, the necessary statement and property are already provided by [1] in its Section 3.7. Then the contribution of this paper is limited in the application of this formulation. Meanwhile, why does the author emphasize the importance of the so-called \"transition constant\" in image reconstruction? After all, the current normalization methods (such as BN, IN, LN, GN, etc.) work well in high-level vision tasks. To my knowledge, IN may indeed not be suitable for image reconstruction tasks (to some extent), but BN and LN used in many low-level vision tasks are appropriate. However, the author only compared IN in their toy experiments, which I find inadequate.\n\n[1] Density estimation using Real NVP, ICLR 2017.\n\n(2) The comparison methods are severely lacking, e.g., DRBL [1], KinD++ [2], Bread [3] for low-light image enhancement and FFA-Net [4], MAXIM [5], UDN [6] and C^2PNet [7] for image dehazing. All the other comparisons have the same problem, and I just name two of them. I do not think comparing two or fewer methods is enough to illustrate the validity of a method. \n\n[1] From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In CVPR, 2020.\n\n[2] Beyond brightening low-light images. IJCV, 2021.\n\n[3] Low-light image enhancement via breaking down the darkness. IJCV, 2023.\n\n[4] FFA-Net: Feature fusion attention network for single image dehazing. In AAAI, 2020.\n\n[5] Maxim: Multi-axis mlp for image processing. In CVPR 2022.\n\n[6] Uncertainty-driven dehazing network. In AAAI, 2022.\n\n[7] Curricular Contrastive Regularization for Physics-aware Single Image Dehazing. In CVPR 2023.\n\n(3) It is unclear why the author chose medical image segmentation and pan-sharpening tasks for downstream applications instead of testing on datasets with low-light or adverse weather conditions, such as DARKFACE [1], EXDARK [2], and RTTS [3].\n\n[1] Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study. TIP, 2020.\n\n[2] Getting to know low-light images with the Exclusively Dark dataset. CVIU, 2019.\n\n[3] Benchmarking single-image dehazing and beyond. TIP, 2019.\n\n\n(4) Please correct the format in lines 143-156, as it appears to be confusing.\n\nTo be concluded, the main drawbacks of this paper are the lack of strong motivation and insufficient experiments, which fail to provide sufficient persuasion. The consistency in the style of the figures and the layout of the paper also need improvement.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and efforts and here we present the rebuttal.\n\n**1. About motivation and contribution.**\n\nThe meaningful of our work: (1) Since IN can normalize different lightness effectively, we have shown that equipping IN as TCN with transition constant ability is useful for image enhancement. (2) Based on it,  the formula statement is a theoretical demonstration to show that TCN satisfies transition-constant, which is necessary. Meanwhile, the toy experiment also validates that the TCN indeed satisfies this property experimentally. Both are necessary to depict our method. (3) Therefore,  our work is meaningful that brings a new view to improve IN as TCN for image enhancement, and is useful for constructing lightweight models.    \n\nThe importance of transition-constant ability: (1) Experimentally, IN would lead to information loss [1] and huge representation ability changes[2][3], therefore, what we address for “transition constant” is to equip IN with this property for image enhancement, and the toy experiment of IN depict it. (2) Previous work[4] shows that the invertible transition-constant also benefits image enhancement. Here, we focus on equipping normalization (especially IN) and making it more suitable for image enhancement. We will further clarify the above points. \n\nAbout IN and other normalizations: (1) IN is suitable: IN has the strongest ability to change information representation than other normalizations [2], and is often employed for style-transfer tasks while other normalization seldom does it, thus improving IN is more suitable for image enhancement (related to process style).  (2) BN and LN are not the focus of our paper: BN and LN less affect information representation ability according to [2], therefore, previous work can adopt them in some low-level backbones. However,  IN is more suitable than BN and LN for style-based tasks (such as style transfer), and although IN can not be used for image enhancement (which is related to enhancing style) directly,  what we do in this paper is to develop IN with TCN to be suitable for image enhancement. Moreover, In supplementary (Table 7), we have also shown that the IN-based approach has better performance for image enhancement than the BN-based approach. (3) More about LN:  We have provided results of applying TCN on the NAFNet (it has LN) in supplementary (Table 2), depicting that TCN can further improve performance with LN. As suggested, we also supplement the toy experiments about other normalization (BN can transit information refers to [2]) to make it clearer, we will add them and more discussions in revision.\nMethods |PSNR|\n|----|----| \n|LN|48.67|\n|TCN(LN)|53.42|\n|GN|41.53|\n|TCN(GN)|51.74| \n|None|54.61| \n\n**2. About experiments.**   \n\nExperiments number: (1) We have conducted 3 baselines for most tasks including the experiments in the supplementary. (2) We have conducted 5 image enhancement tasks and 2 extensive tasks, which are enough. (3) For TCN-Net, we also provide more comparison results in supplementary (Fig. 9) with 8 methods including KIND++, demonstrating its good trade-off between efficacy and effectiveness.  \n   \nWe also need to illustrate that the contribution of our method is to bring a new perspective that enables normalization technique (especially IN) can be useful for image enhancement. Therefore, experiments among three backbones can illustrate it.\nAs suggested, we also provide more results with the suggested methods. Since DRBL (we denote as DRBN) has been provided in the main part and there’s no code of UDN, we provide the results of other methods on the LOL dataset and Indoor dataset here. \n|Model|Methods|PSNR|SSIM|\n|----|----|----|----|\n|KIND++| Original|21.30|0.823|\n||+IN|22.10|0.824|\n||+TCN|23.03|0.829|\n| Bread|Original|22.96|0.838|\n||+IN|22.85|0.836|\n||+TCN|23.26|0.841|\n\n|Model| Methods|PSNR|SSIM|\n|----|----|----|----|\n| FFA-Net|Original|36.39|0.989|\n||+IN|35.84|0.987|\n||+TCN|36.77|0.990|\n| MAXIM| Original|38.11|0.991|\n||+IN|37.49|0.987|\n||+TCN|38.54|0.992|\n|C^2PNet|Original| 42.56|0.995|\n||+IN |42.02|0.994|\n||+TCN|42.80|0.996|\n\n**3. About extensive experiments.**\n\nThe reasons why we conduct the extensive experiments are: (1) Since TCN is proposed to extract lightness (a kind of style) invariant feature while keeping information transition-constant, the extensive tasks are conducted to verify it can benefit more tasks that also related to style information processing. (2) Specifically, for pan-sharpening, it aims to fuse two style images, and we hope TCN can extract their invariant information with information preserving; For medical segmentation, there often exists a domain gap of training and testing sets, and we hope TCN’s robust feature extracting ability can improve the testing accuracy. (3) Besides (2), we have conducted these two tasks before, and other tasks that are related to style information could be benefited from the TCN. We will supplement this discussion in revision.\n\nAlthough we do not mention testing on downstream applications, which is not our goal and duty. As suggested, we can present related results below, where we perform experiments in DARKFACE on S3FD(following setting in SCI[CVPR 2022])   due to the time limit. We will discuss more in revision. \n\n|Model|Methods|mAP| \n|----|----|----|\n|DRBN| Original|0.559|\n||+IN|0.574| \n||+TCN|0.587| \n\n**4. About layout.**\n\nWe follow the layout of previous works[2][3], where tables and figures are embedded with words. We will further revise the paper.\n\n[1] Jin et al. Style normalization and restitution for generalizable person re-identification. CVPR 2020.\n\n[2] Antoine et al. Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence. NeurIPS 2021.\n\n[3] Ekdeep et al. Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning. NeurIPS 2021.\n\n[4] Zhao et al. Deep symmetric network for underexposed image enhancement with recurrent attentional learning. ICCV 2021."}, {"review_id": "K5OyNVItu6", "reviewer": "Reviewer_5FQQ", "summary": "The authors introduce a series of normalization formats by incorporating the transition-constant constraint design. The motivation behind the work is presented, extensive experiments across multiple image enhancement tasks are carried out. ", "questions": "Please see [Weaknesses], authors need to answer the above problems.", "limitations": "The paper comprises the limitation part and addresses points from different aspects. ", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "i. The idea is interesting. With the introducing of invertible mechanism, normalization operation achieves constant information transition. Its property is satisfied with the requirement of image enhancement while reducing the negative effect of blocking information conveying.\nii. The authors presented the method design from motivation analysis and concrete implementation. They have conducted experiments across diverse scenarios, showing the method's efficacy and its wide range of applications.\niii The paper is well organized and easy to be understood. Notably, some information is important when reviewing supplementary materials.\n", "weaknesses": "i. Authors presented the motivation，but it seems that achieving information transition can be accomplished through alternative manners. For example，adding a residual pass from the pre-normalized features could also serve this purpose. Nevertheless, authors have not provided a thorough discussion.\nii. Meanwhile，transformer that contains layer norm is a specific case of above discussed problems. Authors should also discuss it.\niii. In Eq.4，authors assume the lightness changes rule. Nevertheless, quantities of image enhancement tasks may not follow this rule. Indeed, normalization is able to normalize different representation。\niv. Some conducted baselines are too simple to prove the effectiveness of the introduced technique, such as SID. \n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and efforts and here we present the rebuttal.\n\n\n**1. About adding a residual for normalization.**\n\n In fact, adding a residual path is not a reasonable manner: Firstly, it adds the original information to the normalized one directly, which does not consider their relationship for conveying information and thus is not transition inconstant; Secondly, adding the original information and the normalized one would blend their information, leading to the network to learn to reduce the blend effect that integrates them properly, thus is not effective. \n\n\n**2. About discussion the LayerNorm.**\n\nThere are several reasons that the LayerNorm(LN) is not a good format as the TCN. First, IN has the best ability to activate the features [1] that are normalized to lightness-invariant (thus style transfer often employs IN[2] instead of LN), and features are lower activated to LN.  Second, LN in the transformer is for stabilizing the training procedure [3], its role is different from what we explore for image enhancement and the residual connection is not designed for our goal as depicted in the above answer. Third, experiments of NAFNet with LN in supplementary (Table 2) show that the TCN can improve its performance, showing our TCN is compatible with the LN for improving image enhancement.  Moreover, we also present an experiment on the MIT-FiveK dataset by applying LN on the DRBN here, depicting applying LN directly has limited performance compared with TCN.\n\n | Model | Methods |PSNR | SSIM |\n|----|----|----|----|\n| DRBN| Original | 22.11 | 0.868  |\n|        | +LN| 22.84  | 0.874 |\n|        | +TCN | 23.98  | 0.885 |\n\n[1] Antoine et al. Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence. NeurIPS 2021.\n\n[2] Vedaldi et al. Instance Normalization: The Missing Ingredient for Fast Stylization. arXiv:1607.08022\n\n[3] Yu et al. MetaFormer Is Actually What You Need for Vision. CVPR 2022.\n\n\n**3. About depicting the normalization ability.**\n\nWe present Eq. 4 to further demonstrate that normalization has the ability to normalize various lightness besides Fig. 1. Other image enhancement formats are complicated and are not easy to present. After all, since the enhancement task aims to change the overall style to visually pleasant, normalization is suitable for normalizing styles as depicted in style transfer works. \n\n\n**4. About Experiments.**\n\nThanks for your suggestion. As depicted in supplementary (Table 2), we have performed experiments in some recent and complicated architectures such as NAFNet and PFFNet. Here, we present the results of another two recent backbones Restormer (CVPR 2022) and Bread (IJCV 2023) on the LOL dataset in low-light image enhancement to demonstrate the effectiveness. It is important to emphasize that the core of our method is to improve the performance of existing works rather than provide a SOTA, and the lightweight TCN-Net is also useful. We will add more discussions about it in the revision.\n\n| Model | Methods |PSNR | SSIM |\n|----|----|----|----|\n| Restormer| Original | 22.24 | 0.828|\n|        | +IN| 22.06  | 0.824  |\n|        | +TCN | 22.47| 0.831|\n| Bread|Original|22.96|0.838|\n||+IN|22.85|0.836|\n||+TCN|23.26|0.841|"}, {"review_id": "wAElY9mBWT", "reviewer": "Reviewer_yudq", "summary": "This paper presents a novel Transition-Constant Normalization (TCN) for various image enhancement tasks. It has several formats and is plug-and-play for existing image enhancement networks. In the experimental part, proposed method show the extensibility and superiority of the proposed method on both quantitative and qualitative comparisons.", "questions": "I have several concerns here:\n1. Can authors explain why the existing method is not suitable to use normalization although there exists Dncnn and RepSR.\n2. Can authors provide more recent baselines to demonstrate the effectiveness of the TCN?\n", "limitations": "Yes, authors have discussed the limitations at the end of the main body. I am also curious about whether the proposed method could be applied in other scenes.", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "There are several strengths here:\n1. Overall, the motivation is clear presented and seems to be reasonable. The customized design of normalization technique could be a meaningful point.\n2. The method design is reasonable and comprises various formats, the inner key design is to ensure transition-constant, making the concrete format of the method is flexible when applying in specific tasks.\n3. Experiments verify the effectiveness of the proposed method, and exhibit good extensibility in various scenes.\n", "weaknesses": "There are several weakness here:\n1. Although the work is based on designing transition-constant normalization, previous methods have employed normalization in low-level vision tasks. For example, Dncnn incorporate batch normalization and RepSR also employ batch normalization and show it can improve performance. These raised cases make the statement of the proposed method is a little puzzled.\n2. The main figure of describing the method is not straightforward to show its transition-constant, I need to read very carefully to figure out its mechanism.\n3. The baseline that incorporates the TCN is old in some tasks such as low-light image enhancement tasks, this could bring doubt whether the TCN is compatible with more recent architectures.\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your time and efforts and here we present the rebuttal.\n\n**1. Discussion with BN.**\n\n Firstly, BN is a kind of normalization that has the best capability of keeping information representation property [1]. Therefore, BN can transit almost all information and thus can be employed for some low-level vision tasks directly. Differently, other normalization formats especially IN would destroy information representation according to [1], which is different from BN. Secondly, for image enhancement, IN is useful for capturing lightness-consistent representation according to Fig.1, it depicts IN has potential for image enhancement once with transition constant property. Meanwhile, BN is not very effective for capturing low-level statistics and thus is not suitable for image enhancement as shown in supplementary (Table 7). This is what our work is different from the mentioned two works. We will further clarify this point in the updated version.\n\n [1] Antoine et al. Proxy-Normalizing Activations to Match Batch   Normalization while Removing Batch Dependence. NeurIPS 2021.\n\n**2. The main figure is not straightforward.**\n\nThanks for pointing out this point. In fact, we present the main figure to demonstrate its procedure, and its relevance to the transition-constant property has been proved in supplementary(Fig. 3). We will clarify the relation of the main figure to Fig. 3 in supplementary in the updated version to make it clear.\n\n\n**3. More results applying TCN in recent baselines?**\n\nThanks for your suggestion. We have already presented results on the recent method NAFNet(ECCV 2022) in the supplementary. Due to limited time and space, here we present the results of Restormer (CVPR 2022) and Bread (IJCV 2023) on the LOL dataset here. We will add these results in the revision. Note that we also propose a lightweight TCN-Net constructed by the TCN and it achieves a good trade-off between effectiveness and efficacy.\n\n| Model| Methods|PSNR|SSIM|\n|----|----|----|----|\n| Restormer|Original|22.24|0.828|\n|        |+IN|22.06|0.824|\n|        |+TCN|22.47|0.831|\n| Bread|Original|22.96|0.838|\n||+IN|22.85|0.836|\n||+TCN|23.26|0.841|\n\n\n**4. Extensive ability.**\n\nSince TCN is developed for the style-based task of image enhancement, we think that the TCN can also be extended to other tasks about style processing, such as domain adaptation and multi-sensor image fusion. The extensive experiments on pan-sharpening (fusing two style images) and medical segmentation (there’s a domain between training and testing) can illustrate it. "}]

Author rebuttal:

Meta-review: This paper introduce transition-constant normalization (TCN) to harness the potential of applying normalization techniques for image enhancement. The scores from all reviewers are positive and most reviewers find the approach interesting and the results are convincing. The AC agrees with the reviewers on accepting the paper.
Decision: Accept (spotlight)