Title: Transition-constant Normalization for Image Enhancement
Venue: NeurIPS 2023
Accepted: yes
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

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 https://github.com/huangkevinj/TCNorm .

1 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, 3, 4, 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, 7, 8]. 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

PSNR

6.07dB

PSNR

26.66dB

Overexposure

Underexposure

Underexposure

Overexposure

IN for overexposure

IN for underexposure

𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛

(e.g.  IN)

TCN

Forward (we used)

Inverse

Transition-constant of invertible

Transition-inconstant of non-invertible





Transit all

information

of       ?

𝑭

𝑭

𝑭



Transit all

information

of       ?

𝑭



(a) Apply normalization for different exposure images.

(b) Transition-constant property of normalization.

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 as 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 to capture image style through statistical representation [9]. Image enhancement, which aims to restore lightnesscorrupted images to their normal versions, can be viewed as a style transformation inherently linked to normalization techniques (see Fig. 1 (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 a novel operation called transition-constant 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. 1 (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 no parameters, making it a 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 performance improvement; (2) Plug-in capability in encoder-decoder architectures for downsampling and recomposition of information; (3) Construction of a 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 Transition-constant Normalization (TCN) by organizing normalization operations to satisfy the invertible mechanism, ensuring constant feature normalization and information transition. 3) Our proposed TCN is 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).

Transit all information

of       ?

𝑭



Transition- constant for

𝑭

?



Recent years have witnessed rapid development in the related areas [10, 11, 12, 13]. For low-light image enhancement, algorithms are designed to enhance the visibility of images captured under lowlight conditions [14, 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 [15, 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 [33, 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 a long time [38, 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-level statistics of features, which has been widely employed in style transfer tasks [42, 43]. Some other variants of normalization, including Layer Normalization (LN) [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 ∈ R N × C × H × W , where N , C , H and W represent batch size, channel numbers, the spatial height and width, respectively. Let x ncij and ˆ x ncij denote a pixel before and after normalization, where n ∈ [1 , N] , c ∈ [1 , C] , i ∈ [1 , H] , i ∈ [1 , W] . Without taking considering into affined parameters, we can express normalization operation as:

where µ k and σ k denote the feature mean and standard deviation, ϵ is a small constant to preserve numerical stability. k ∈ { IN, BN, LN, GN } is to distinguish different normalization formats. Within the above normalization family, the calculation of µ k and σ k is different and are expressed as:

IN : I k : I IN = { ( i, j ) | i ∈ [1 , H] , j ∈ [1 , W] } ;

BN : I k : I BN = { ( n, i, j ) | n ∈ [1 , N] , i ∈ [1 , H] , j ∈ [1 , W] } ;

LN : I k : I LN = { ( c, i, j ) | c ∈ [1 , C] , i ∈ [1 , H] , j ∈ [1 , W] } ;

where I k is a set of pixels, | I k | denotes the number of pixels and G is the group division.

Within deep neural networks, Eq. 1 is often affined with scaling and shifting parameters α and β :

It is well-known that lightness can be considered as a kind of style and Instance normalization can facilitate consistent style information [9] and image enhancement network optimization due to bridging the gap of different lightness representations.

Verifying normalization effect for lightness . Given an image x a and its lightness-adjusted version x b , the relationship between x a and x b can be expressed in correction procedure [46, 47, 48] as:

where Λ is a linear transformation and γ is for global non-linear adaption and is close to 1 when content is not severely changed. Therefore, the p-norm distance between their normalized versions is:

Therefore, normalization reduces the distance of different lightness, which is also validated in Fig. 1(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.

Transition-inconstant of Normalization. Referring to Norm( · ) in the Eq. 1 as f , its Jacobian matrix is expressed as:

Therefore, the calculation of the above Jacobian matrix is det ( J f ( x ) ) = 0, denoting the normalization operation is not invertible and resulting in transition-inconstant. Meanwhile, in practice, previous works have demonstrated that IN would lead to severe information loss [42] and huge representation ability changes [50, 51], while LN and BN can keep almost all of 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 to introduce a new mechanism that enables the IN can keep the information representation ability for image enhancement.

3.2 Transition-constant 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. To this end, we introduce the TCN as shown in Fig. 2, 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 ∈ R B × C × H × W is firstly subsampled to F s ∈ R B × 4C × H 2 × W 2 according to the unshuffle operation [52] as shown in Fig. 2 (a), which is:

where a and b denote the subsampling index. We divide F s into two features F 1 and F 2 with two groups according to the sampling index ( i, j ) as:

where Concat( · , · ) denotes the concatenate operation along the channel dimension.

Then, we calculate the mean µ 2 and standard deviation σ 2 of one stream feature F 2 in IN format, which are derived by setting I k in Eq. 2 as I IN :

These statistics are utilized to normalize the feature F 1 as the output in this stream:

Next, we subtract the feature F 2 and ˆ F 1 and obtain the output of another stream:

Finally, the two stream features are sampled with pixel shuffle operation to the original resolution with the shape of [N , C 2 , H , W] . They are further concatenated in the channel dimension as ˆ F ∈ R B × C × H × W , which is the output of the TCN. This procedure is expressed as:

where Pixshuffle( · ) denotes the pixel shuffle operation [53]. We verify the above procedures satisfy the transition-constant and normalization ability as below, respectively.

1) Verify the transition-constant ability. We validate the above two-stream design satisfies the invertible procedure and thus is transition-constant. To this end, Eq. 10 and Eq. 11 are re-written as:

where S ( · ) and M ( · ) denote standard deviation and mean functions, ⊘ denotes element division.

Inspired by the proof in RealNVP's [54] transformation, we need to calculate the Jacobian matrix of Eq. 13 (denote it as g ), which is more intuitively written as:

We derive its Jacobian matrix (detailed in the supplementary) as:

Here, the above Jacobian matrix is further calculated as:

̸

Upon the det ( J g ) = 0 , it indicates that J g 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.

̸

Verify the normalization ability. The normalization ability of the TCN is guaranteed by the pixel unshuffle operation in Eq. 7, leading to the same statistics of F ab s [55, 56]. Therefore, we have µ 2 ≈ µ 1 , σ 2 ≈ σ 1 , and the Eq. 10 is thus converted to:

Therefore, it has the same format as Eq. 1, demonstrating that the operation in Eq. 10 has the normalization ability as the IN. Further, we verify the above rules by the toy experiment (see Sec. 4.1) in Fig. 4 and Fig. 5.

Discussion. The core of the TCN is the statistic calculation manner of µ 2 and σ 2 in Eq. 9 which can be generalized in a unified calculation manner in Eq. 2 and derive from other normalization formats of TCN, shown in Table 1. 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 in the supplementary. We provide more discussions in the supplementary.

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. 2 (a)) for image enhancement based on calculating statistics in Eq. 9 of IN format, which is plug-and-play for networks.

The affined TCN. We extend the original TCN by introducing affined parameters α and β to the normalization procedure, resulting in the affined TCN (Fig. 2 (b)). We incorporate learnable shifting

𝝁𝟐 =

σ𝑭𝟐

𝑯𝑾

𝝈𝟐 =

𝑯𝑾

෡

𝑭𝟏 =

𝑭𝟏 - 𝝁𝟐

𝝈𝟐 𝟐 +𝝐

𝝁𝟐

𝝈𝟐

𝑭𝟐

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෡ 𝑭

Un-shuffle & Split

Concatenate & Pixel Shuffle

TCN

𝝁𝟐 + 𝜷

+

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Skip TCN

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𝑭𝟏 × 𝝈𝟐 + 𝝁𝟐

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pixel

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C

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down

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Affined TCN

Transition-constant Normalization (TCN )

parameter β and scaling parameter γ into µ 2 and σ 2 in Eq. 9:

where µ ′ 2 and σ ′ 2 represent the affined statistics. Then, we substitute Eq. 18 to Eq. 1:

Since ϵ is a small constant near to 0, the Eq. 19 can be approximated as:

Eq.20 shares a format similar to the affined normalization, with γ and β ′ as the learnable scaling and shifting parameters in Eq. 3. The affined TCN seamlessly integrates into image enhancement networks, serving as a plug-and-play solution. Notably, it maintains the information transitionconstant property, as discussed in detail in the supplementary material.

The skip TCN. From Eq.10 and 11, the TCN generates two types of features: a domain-invariant lightness consistent feature ˆ F 1 and a domain-variant lightness inconsistent feature ˆ F 2 . Previous studies [57, 58] have demonstrated the effectiveness of incorporating the domain-variant component into deep encoder-decoder networks while skipping the domain-invariant component to the decoder layer. In this work, we propose the skip TCN architecture, illustrated in Fig. 2 (c).

Given a feature F in an encoder layer, we convey its lightness inconsistent feature ˆ F 2 , obtained from Eq. 10, to the downsampled deeper layer that derives F d using the following expression:

where Down 2 means downsampling with a factor of 2. While for the lightness consistent feature ˆ F 1 derived in Eq. 11, we skip it to the corresponding decoder layer feature F u with the statistic µ 2 and σ 2 derived in Eq. 9. We integrate them by inverting the operation of Eq. 10 and Eq. 11:

Skip TCN

Skip TCN

Skip TCN

Affined

TCN

Affined

TCN

Convs

Convs

Convs

Convs

Here, the upsampling operation with a factor of 2 is denoted as Up 2 , and F uo 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 provided in the supplementary material.

Construct a very efficient TCN-based Network. We introduce TCN-Net, an efficient network architecture depicted in Fig. 3, which combines affined TCN and skip TCN. This framework adopts an encoder-decoder-based architecture with vanilla convolution blocks to depict the effectiveness of the TCN. Further details and discussions are available in the supplementary material.

4 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.

Decoder

Loss

Encoder

Features

𝑇𝐶𝑁

Respectively Select

Optimization

60

54.61

55

50.53

50

PSNR(dB)

45. 40

35

30

29.58

25

None

IN

TCN

t-SNE

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of Underexposure

𝑭

of Overexposure

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of Overexposure

෢

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of Underexposure

Clustered

Non-Clustered

Underexposure Input

Underexposure's

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Underexposure's

෢ 𝑭 𝟏

Overexposure's

𝑭

Overexposure's

෢ 𝑭 𝟏

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一

4.1 Toy Experiment

To illustrate the proposed TCN has the ability to normalize the features while transition-constant, we introduce a toy experiment as shown in Fig. 4 : we construct an encoder-decoder-based architecture for reconstructing the input image, where the TCN and other normalization formats are inserted between the encoder and decoder as different versions. Then we train this self-reconstruction architecture on 1000 samples from MIT-FiveK dataset [59] until its convergence and test the self-reconstruction 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 transition-constant property. Furthermore, we test 100 underexposure samples and 100 overexposure samples from the SICE dataset, and we provide the feature distribution of F and ˆ F 1 (input and normalized output of the TCN) in the left part of Fig. 5 , as well as feature maps in the right part of Fig. 5. The different exposure features processed by the TCN get to be

Training on SICE dataset

20

15

PSNR(dB)

10

SID

SID+IN

SID+TCN

5

SID+Affined-TCN

SID+Skip-TCN

0

0

20000

40000

60000

80000

100000120000140000

Iterations

clustered, demonstrating the normalization ability of the TCN for extracting the lightness-consistence 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 [14] as baselines.

Exposure Correction. Following [62], we adopt MSEC dataset [24] and SICE dataset [63] for evaluations. The above two architectures, i.e., DRBN [15] and SID [14] are regarded as baselines.

SDR2HDRTranslation. Following [30], we choose the SRITM dataset [31] and HDRTV dataset [30] for evaluation. We employ the structures of NAFNet [64] with its three basic units as the baseline 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 [66] as the baseline for validation.

4.3 Implementation Details

Since there exist three TCN formats in Sec. 3.3, we respectively integrate them into the baseline to conduct experiments. For comparison, we perform the experiments of 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 the original settings, and our TCN-Net until it converges. More implementation details are provided in the supplementary.

Input

DRBN

DRBN+TCN

Ground truth

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 2 to Table 5, where the best and second-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 2). 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 on the MIT-FiveK dataset [59] due to the limited space. As shown in Fig. 7, the integration of the TCN leads to a 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 to 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, we introduce another two tasks that are 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 a style domain gap between training and testing sets.

Extension on medical segmentation. We apply the TCN on the UNet [67] and AttUNet [68] 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 the original TCN to the GPPNN [70] and PANNet [71] baselines in the pan-sharpening task, which is a common task in guided image super-resolution. We integrate it when extracting pan and multi-spectral features, and experimental results on WorldView II dataset [72, 73] in Fig. 8 suggest the effectiveness of the TCN.

5 Limitation and Discussion

Firstly, we validate the effectiveness of TCN in image enhancement tasks, while the investigation of applying TCN to other image restoration tasks will be explored in the future, such as the allin-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 transition-constant, such as image fusion tasks [76]. Finally, the TCN could introduce very few computation burdens although it is free of parameters, which is negligible compared with its bring performance improvement. Note that the focus of this work is beyond introducing a plugand-play operation to existing networks for performance gain. The introduced TCN can be a new choice of normalization and feature disentanglement, which excavate consistent representations while preserving information when developing a new model that requires this property.

6 Conclusion

In this paper, we introduce a new 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, it keeps the normalization ability for capturing lightness consistence representations. The proposed TCN is a 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 a high value of research and application. Our method introduces a 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 a dim light effect, which would be eliminated by the image enhancement algorithm. In these cases, it is suggested to combine the users' preferences to achieve customized image enhancement effects.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\nThere 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\nWEAKNESSES:\nThere 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\nQUESTIONS:\nI 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\nLIMITATIONS:\nYes, 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.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\ni. 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\nWEAKNESSES:\ni. 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\nQUESTIONS:\nPlease see [Weaknesses], authors need to answer the above problems.\n\nLIMITATIONS:\nThe paper comprises the limitation part and addresses points from different aspects.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\nThe paper covers a range of tasks in the experiments. The module design is simple and achieves comparable speed to previous feature normalization methods.\n\nWEAKNESSES:\n(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.\n\nQUESTIONS:\nPlease see the Weaknesses.\n\nLIMITATIONS:\nThis paper has no apparent negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS:\nThis 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.\n\nWEAKNESSES:\n(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.\n\nQUESTIONS:\nIf 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.\n\nLIMITATIONS:\nYes. Limitations are adequately addressed.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n1. 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.\n\nSTRENGTHS:\nIt 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\n\nWEAKNESSES:\nHowever, 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.\n\nQUESTIONS:\nWhy 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?\n\nLIMITATIONS:\nThe limitations and potential negative societal impact of their work have been addressed in this paper.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-06"}