Title: Rethinking Nighttime Image Deraining via Learnable Color Space Transformation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Nighttime image deraining

Benchmark dataset

Color space transformation
Abstract: Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.

Rethinking Nighttime Image Deraining via Learnable Color Space Transformation

Abstract

Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net .

1 Introduction

Images degraded by complex nighttime rain significantly affect downstream vision tasks, such as autonomous driving and video surveillance [8, 35, 58]. Unlike daytime images, nighttime scenes present unique challenges due to low illumination and varying light sources, which together amplify the visibility and complexity of rain artifacts. Thus, it is of great interest to develop an effective algorithm to recover high-quality rain-free images from nighttime scenarios. Recently, several efforts [35, 22, 7, 24, 16] have been made to address nighttime image deraining, with the emergence of some synthetic datasets, including GTAV-NightRain [53] and Raindrop Clarity [30]. However, we note that most existing synthetic datasets [2, 46, 21] feature a global uniform distribution of rain, as random rain masks are added linearly to nighttime backgrounds. This naturally leads to

100000

100000

000001

100000

300000

300000

300000

300000

50000

50000

50000

50000

200000

200000

200000

200000

100000

10

20

30

100000

0

10

20

30

100000

0

10

20

30

100000

10

20

30

Rainy image

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

Rain-free image

D

0

100

200

0

100

200

0

100

200

0

100

inadequate realism and harmony in the visual appearance, which limits the ability to train better nighttime deraining models.

In fact, nighttime scenarios lack uniform global illumination, with darkness and irregular artificial light sources dominating the visual landscape [18, 31, 56]. This results in markedly different appearances and visibility distributions of rain effects, which are typically concentrated around light sources and only visible under specific lighting conditions.

Based on these observations, we design a new nighttime rainy image composition pipeline, and contribute a high-quality benchmark dataset, HQ-NightRain. In our pipeline, we fully consider the visibility of rain under varying illumination conditions. Specifically, our approach involves associating the rain mask with an illumination coefficient matrix of the nighttime background image, thereby generating a non-uniform nighttime rain distribution to more realistically adapt nighttime scenes. By this way, we ensure the nighttime rain is visually more compatible with the corresponding background scenes, aiming at reducing the domain gap between synthetic and real-world datasets.

Armed with this dataset, our focus shifts to exploring an effective algorithm for nighttime image deraining. We note that existing nighttime deraining approaches [35, 54] do not take into account the inherent properties of nighttime rainy images. In other words, these methods still perform image deraining in the RGB color space. Based on the pixel value statistical analysis in Figure 1, we find that nighttime rain is most pronounced in the Y channel. The reason behind this lies in the fact that the Y channel in the YCbCr color space captures luminance information. In low-light conditions, rain often reflects light from artificial sources, creating high-contrast patterns that stand out sharply against the dark background. This makes the differences between rainy and rain-free images more prominent in the Y channel compared to the RGB channels. Therefore, this motivates us to develop an effective approach performed in the Y channel, tailored for nighttime image deraining.

To this end, we propose an effective color space transformation framework CST-Net for nighttime image deraining. Specifically, we develop a learnable color space converter that transforms the input from the RGB space to the YCbCr space to perform rain degradation removal. This enables the model to adaptively allocate the parameters required for color space transformation across various nighttime images, resulting in better robustness in real-world scenarios. To better represent illumination information, we introduce implicit illumination guidance to enhance nighttime rain removal. Extensive experiments demonstrate that our method achieves favorable performance against state-of-the-art ones on our proposed dataset and public benchmarks. The main contributions are summarized as follows:

We construct a high-quality nighttime image deraining benchmark dataset HQ-NightRain, which further improves the harmony and realism of synthetic images.

We propose a robust learnable color space transformation framework for nighttime image deraining, which explores the potential of the Y channel in rain removal.

Wedemonstrate the effectiveness of our dataset, and show that our proposed method achieves favorable deraining performance against state-of-the-art ones.

2 Related Work

Image deraining . Recent years have witnessed significant advancements in image deraining, driven by the emergence of numerous benchmark datasets and deep learning models [7, 25, 26, 41]. We note

Fusion)

Rain streak mask (RS1)

Rain streak mask (RS1)

Rain streak mask (RS2)

Rain streak image (ORs)

Start

Rain streaks

Rain Width

Rain Length

Rain Direction

RainDensity

Transparency

Illuminance

Coefficien

Matrix (1)

0.5

0.3

Rain streak image (ORs)

Background (B)

Raindrops

Background (B)

(Fusion)

Raindrop mask (RD1)

Raindrop image (Oro)

Raindrop mask (RD1)

Raindrop mask (RD2)

Raindrop image (ORo)

(a)Previous method

(b)Ourmethod

existing image deraining efforts primarily focus on daytime scenarios [47], with comparatively little attention given to nighttime deraining. For the nighttime deraining dataset , Zhang et al. [53] first proposed the GTAV-NightRain dataset for nighttime rain streak removal, using ray tracing technology on a gaming platform to render rainy scenes. Li et al. [35] synthesized the RoadScene dataset using a GAN-based method, specifically designed for nighttime driving scenarios in rainy conditions. Recently, Jin et al. [22] constructed a dual-focused dataset for day and night raindrop removal. Although these datasets have been proposed, most synthesis methods rely on approaches used for daytime rainy scenes, resulting in a large domain gap between synthetic and real images. Instead, we build a high-quality benchmark by taking into account illumination properties of nighttime images.

For the nighttime deraining method , Zhang et al. [54] proposed a rain location prior (RLP) that employs a recurrent residual network to learn the positional information of rain streaks from nighttime rainy images. Lin et al. [30] developed a teacher-student framework with an adaptive deraining module and an adaptive correction module to remove rain from nighttime videos. Although these methods have made preliminary explorations in nighttime deraining, they overlook the illumination properties of nighttime rain and continue to perform deraining in the RGB space, similar to daytime deraining approaches [6, 4]. Different from these methods, we leverage a learnable color space transformation to perform rain removal in the YCbCr color space.

Color space transformation . Color space transformation refers to the process of converting an image from one color space to another to facilitate various image processing tasks. As a result, color spaces like YCbCr, HSV, HVI, etc. , have been explored in recent years [23, 38, 43]. For example, the RGB color space uses red, green, and blue components, while the YCbCr space separates luminance (Y) from chrominance (Cb and Cr). In nighttime rainy images, rain is often more pronounced in the Y channel compared to the RGB channels. Nighttime rain can significantly affect the overall brightness, making them more visible in the Y channel [17]. In this work, by focusing on the Y channel, we can better distinguish between rain and background, thus enhancing the effectiveness of nighttime deraining models.

3 Dataset Construction: HQ-NightRain

Existing nighttime deraining datasets [35, 22, 53] often lack realism and visual harmony due to the linear addition of rain effects onto nighttime backgrounds. In this paper, we develop an illuminationfused image synthesis method to generate more realistic nighttime rainy images.

3.1 Nighttime Rain Model

Most previous studies [21, 35, 22] use a linear superposition model to synthesize rain streak images R s and raindrop images R d . Mathematically, it can be expressed as:

where B represents the rain-free nighttime background, S is the rain streak mask, D is the raindrop mask, M is a binary mask, and ⊙ denotes element-wise multiplication.

Real-world Rain

HQ-NightRain

(Ours)

RoadScene-rain

UAV-Rain1k

Raindrop Clarity

GTAV-NightRain

Small

Domain

Gap

(a) Comparison of different nighttime rainy visual samples

(b) Distributions of t-SNE

40

SHARK

30

20

10

RoadScene-rain

GTAV-NightRain

HQ-Rain

0

10

Large Domain Gap!

20

30

HQ-NightRain(Ours)

Real-worldNighttimeImage

-30

-20

-10

0

10

20

30

40

In this work, we rethink the formation of nighttime rainy images by examining the visibility and distribution characteristics of nighttime rain effect [31]. During the day, light is produced by the sun, which can be considered parallel and uniform, making rain clearly visible. At night, the primary light sources are artificial ( e.g. , street lamps, traffic lights, vehicle headlights), and the light radiates outwards from these sources. According to the first law of illumination [9], the illuminance on a surface is inversely proportional to the square of the distance from the light source. Therefore, rain in nighttime scenes is only visible near light sources and is not noticeable in low-illumination areas.

Based on this observation, we propose a new rain model for nighttime rain imaging, defined as:

where the function σ ( · ) incorporates illumination information into the rain streak and raindrop masks, f [ · ] represents the 3 × 3 convolution function used to merge the background with rain mask, and ρ ( · ) denotes the defocus blur operation.

3.2 Data Construction Pipeline

We present the data construction pipeline of HQ-NightRain in Figure 2. Compared to existing nighttime rainy datasets that only consider uniform rain distribution, our approach integrates lighting information from nighttime scenes, allowing the appearance of rain streaks and raindrops to be nonuniformly distributed based on illumination, which is commonly observed in real-world scenarios.

Illumination coefficient estimation . To fully account for the characteristics of nighttime images, we first extract the illumination coefficient matrix from the background image. Specifically, we convert the background image B from the RGB color space to the HSV color space. Then, we extract illumination information from the V channel of the background image. This process is defined as:

where N denotes the initial illumination matrix, T denotes the color space transformation, f v denotes the extraction of the V channel, and Norm is the normalization function.

Rain is not only invisible in low-illumination areas but also in excessively high-illumination areas, such as at the center of a light source. To this end, we apply a masking operation Mask ( · ) to N , setting high and low illumination thresholds to identify the illuminated regions where nighttime rain is visible. The final illumination coefficient matrix I is expressed as follows:

where v is the value at position ( x, y ) in matrix N , τ 1 and τ 2 are denote the illumination thresholds.

＋

𝐼 𝑌

𝐼 𝐶𝑏

𝐼

𝐶𝑟

Rainy input

(𝐼𝑅𝐺𝐵)

CSC

Output

(𝑂 𝑅𝐺𝐵

)

𝐼𝑅𝐺𝐵

CSC

𝐼ത 𝑌

𝐼

′

𝑅𝐺𝐵

𝐼 ෠ 𝑌

MLP

𝑅

𝐶𝑏

𝐶𝑟

𝑌

𝐺

𝐵

＋

Element-wise

Addition

RGB to YCbCr

Transformation

Degradation Removal Stage

Color Refinement Stage

Element-wise

Concatenation

C

Learnable

Encoder

Enc

MLP

C

𝐼 ෠ 𝑌

P

E

𝐼

𝑌 ′

Column-wise Average Pixel Values

Illumination Intensity

Rain Density

Implicit Illumination Guidance (IIG)

Local

illumination

focusing

High

Low

IIG

YCbCr to RGB

Transformation

RGB ColorSpace

0.8

1.0

0.6

B

0.4

0.2

0.0

0.0

0.2

0.4

R

0.6

0.8

1.0

0.0

0.2

04G

0.6

0.8

1.0

YCbCrColorSpace

255

0

001

200

1.0

0.8

0.6Cr

0.4

Y

0.6

0.2

0Cb

0.6

0.8

1.0

0.0

0.2

Nighttime rain mask blending . We first generate initial rain streak masks (with varying widths, lengths, densities, directions, and transparencies) [7] and raindrop masks (with varying sizes and shapes) [2]. Subsequently, the appearance of the obtained nighttime rain layer is associated with the illumination information of the background image, expressed as:

where σ ( · ) represents the blending of the illumination with the rain mask. Based on real-world observations, when light from sources such as streetlights or car headlights passes through raindrops, it produces refraction and scattering effects, resulting in a defocus blur phenomenon in nighttime rain. Therefore, we also apply the defocus blur function to the rain-free nighttime background, see Eq. (2).

3.3 Benchmark Statistics and Comparisons

The background images in our proposed HQ-NightRain are selected from the public BDD100K [50] dataset, which contains images captured from first-person driving scenes in urban environments. Here, we select rain-free backgrounds labeled as 'night' according to the provided JSON files. In total, our dataset contains 11,200 image pairs, with 10,000 pairs for training, 900 pairs for validation, and 300 pairs for testing. Based on the degradation type of nighttime rain, it is further divided into three subsets, including rain streak (RS), raindrop (RD), and a mixture of rain streak and raindrop (SD). Figure 3 presents comparison results between existing rainy datasets [35, 22, 53, 21] and our benchmark, demonstrating that HQ-NightRain more closely aligns with the distribution of real-world nighttime rainy images. HQ-NightRain carefully considers harmony in synthesized nighttime rainy scenes, which reduces the domain gap between synthetic and real images. We additionally provide a real-captured subset comprising 512 images and a synthetic nighttime rain subset in natural scenes comprising 20 image pairs.

4 Proposed Method

4.1 Overall Pipeline

To better model the complex rain information in nighttime images within the Y channel, we develop an effective color space transformation network CST-Net. Figure 4 summarizes the two-stage architecture of CST-Net, which consists of a degradation removal stage and a color refinement stage.

Given that the distribution of nighttime rain is more distinct in the luminance channel ( e.g. , Y) compared to the color channels ( e.g. , RGB), we first perform Y-channel based image deraining in the degradation removal stage. Specifically, given a rainy image I RGB ∈ R H × W × 3 RGB , where H and W denote the image height and width, we implement color space transformation to facilitate the

switching between RGB and YCbCr spaces. Here, we propose a learnable Color Space Converter (CSC) that transforms the input image from the RGB space to the YCbCr space, splitting it into three channels: I Y ∈ R H × W × 1 Y CbCr , I Cb ∈ R H × W × 1 Y CbCr , and I Cr ∈ R H × W × 1 Y CbCr . Next, we feed I Y into the degradation removal stage to perform rain removal in the luminance channel, while the chrominance channels I Cb and I Cr are directed to the color refinement stage. This process is defined as follows:

where split means splitting the channels, P 1 denotes the first stage, and I ¯ Y ∈ R H × W × 1 Y CbCr is the luminance channel used for degradation removal.

To better handle the complex and random rain degradation near light sources, we further introduce an Implicit Illumination Guidance (IIG) module to facilitate the rain removal process, resulting in I ˆ Y ∈ R H × W × 1 Y CbCr = IIG ( I Y ) . The output is then converted back into the RGB color space by the CSC and sented to the color refinement stage for color restoration. This process is expressed as:

where I ′ RGB ∈ R H × W × 3 RGB represents the output from the first stage converted back to the RGB space, cat [ · ] represents element-wise concatenation, P 2 denotes the second stage, and O RGB is the final derained image.

4.2 Learnable Color Space Converter

As a basic image processing operation, RGB to YCbCr conversion separates the brightness from the color components. This is typically achieved using fixed values W to perform a linear transformation, which is defined as follows:

where ◦ denotes matrix multiplication.

However, this fixed color space conversion is based on a standard paradigm for general scenes. It is difficult to adapt to the complex and random nighttime rain scenarios, especially when the image's brightness is strongly influenced by artificial nighttime lighting sources [56, 11]. To this end, we develop a learnable Color Space Converter (CSC), so that the learned features are robust to complex nighttime rain degradation. Specifically, we introduce a learnable matrix Φ with a shape of 3 × 3. Unlike the fixed-weight linear transformation in W , our proposed CSC has no fixed linear weight matrix. Instead, each parameter is replaced by a learnable one-dimensional variable, enabling more flexibility in the transformation. This matrix is defined as follows:

where φ i,j is the learnable weights at position ( i, j ) , i and j denote the row and column indices.

In fact, rain streaks appear as localized brightness variations, often forming transparent or semitransparent white lines that become more pronounced in the Y channel under low-light conditions due to reflections and refractions. Our proposed CSC enables each conversion weight parameter to be replaced by a learnable one-dimensional variable, non-linearly transformed through a multi-layer perceptron (MLP) [6]. This approach not only retains the ability to process luminance in color space conversion but also allows adaptive adjustment of parameters based on specific datasets and application scenarios. It can accommodate varying lighting conditions, scene types, and content characteristics, making the extracted features more robust to complex and random nighttime rain effects. The overall process is expressed as:

where MLP ( · ) denotes the operation performed by the MLP.

4.3 Implicit Illumination Guidance

To capture illumination information to guide the nighttime rain removal, we introduce an Implicit Illumination Guidance (IIG) module between these two stages. Different from existing approaches that rely on explicit illumination models to achieve illumination estimation [1], we integrate the implicit neural representation into our model to better encode illumination information in complex nighttime scenes.

Specifically, we first leverage a shared encoder from the degradation removal stage to encode features E . The pixel coordinates of each nighttime rainy patch I Y ′ are stored in the corresponding coordinate set P ∈ R H × W × 2 , where the value '2' represents horizontal and vertical coordinates. Given the uneven illumination in nighttime images, we apply dynamic weights for each pixel coordinate and its corresponding features, defined as follows:

where P ( x,y ) represents the local illumination information at the central coordinate ( x, y ) , while Ω( x, y ) denotes the neighborhood region centered around ( x, y ) ; w ( x ′ , y ′ ) is a weight for illumination focusing, with the weight decreasing as the distance from the central pixel increases.

By predicting the Y channel value of each pixel, we fuse encode features E and P ( x,y ) , then feed them into the MLP as a decoding function to obtain the final output image I ˆ Y , which is defined as:

where the MLP is adopted to map coordinates to their predicted Y values, instead of the RGB values used in existing works [6, 44]. Here, fitting the implicit neural representation to reconstruct an image requires finding a set of parameters for the MLP. Diverse illumination distributions yield different sets of parameters, which in turn means the MLP is adaptive to the complex nighttime rainy scenes.

(a) Rainy input

(b) Ground truth

(c) IDT

(d) Restormer

(e) DRSformer

(f) NeRD-Rain

(g) Ours

(a) Rainy input

(b) RCDNet

(c) IDT

(d) Restormer

(e) DRSformer

(f) NeRD-Rain

(g) Ours

订电173426

银话139914

订电173426

银话139914

订电173428

银话139914

订电173426

银话139914

订电173426

银话139914

订电173426

银话139914

订电173426

银话139914

5 Experiments

5.1 Experimental Settings

Datasets and metrics . We conduct the experiments on our HQ-NightRain dataset, a public nighttime deraining dataset GTAV-NightRain set1 [53], and two real-world benchmarks ( e.g. , RealRain-1k [28] and RainDS-real [33]). To evaluate the quality of each derained image, we use PSNR [20, 3], SSIM [40], LPIPS [57], PaQ-2-PiQ [49], and MANIQA [45] as evaluation metrics.

Implementation details . In our CST-Net, both the degradation removal stage and the color refinement stage adopt a 4-level Transformer-based encoder-decoder structure [51]. We conduct training using an NVIDIA GeForce RTX 3090 GPU. The Adam optimizer [13] with default parameters is used. The initial learning rate is set to 2 × 10 -4 and gradually reduces to 1 × 10 -6 using a cosine annealing scheduler. The model is trained for 500 epochs with a patch size of 128 × 128 pixels and a batch size of 4. We set the illumination thresholds τ 1 and τ 2 to 0.2 and 0.8, respectively.

5.2 Comparison with the state-of-the-art

We compare our method with 11 image deraining technologies, including PReNet [34], RCDNet [37], SPDNet [48], MPRNet [52], IDT [42], Restormer [51], SFNet [12], DRSformer [5], RLP [54], MSGNN [36], and NeRD-Rain [6].

Evaluations on the HQ-NightRain dataset . Table 1 reports the quantitative results of different approaches on the HQ-NightRain dataset. All methods are retrained on the proposed dataset for a fair comparison. It can be observed that our proposed CST-Net achieves the highest PSNR and lowest LPIPS values across various types of nighttime rain, demonstrating the effectiveness of our method in nighttime rain removal. Specifically, our method outperforms SOTA method NeRD-Rain [6] by 0.81dB PSNR on the SD subset. In the first row of Figure 5, we further show the visual comparison. Compared to existing methods that still leave residual rain, our method can restore clearer results.

Evaluations on public datasets . Table 1 summarizes the quantitative results on the public GTAVNightRain dataset [53], where our method consistently achieves the best performance. Our method outperforms the NeRD-Rain [6] by 1.1 dB on the GTAV-NightRain dataset [53], demonstrating the

advantage of performing rain removal in the Y channel. As shown in the second row of Figure 5, our method successfully removes rain streaks and restores a clear background.

Evaluations on real-world datasets . We also evaluate our method on the challenging RealRain1k [28] and RainDS-real datasets [33]. Quantitative results in Table 2 demonstrate that our method can effectively handle diverse types of spatially-varying real rain streaks. Figure 6 shows visual comparisons of the evaluated methods, where most deraining methods are sensitive to complex rain streaks in real scenes. Our proposed approach effectively removes nighttime rain, demonstrating better generalization on real-world data.

6 Analysis and Discussion

Real-world generalization with our HQ-NightRain .

To demonstrate that the model trained on our HQNightRain dataset generalizes better to real-world nighttime scenes, we compare the performance of the same model trained on the proposed HQ-NightRain dataset and other synthetic nighttime rainy datasets [35, 53], then tested on real-world rainy images, as shown in Table 3. As presented in Figure 8, the model trained in HQ-NightRain performs better in real-world images, suggesting that our dataset effectively reduces the domain gap between synthetic and real-world nighttime rainy scenes.

Effectiveness of the learnable CSC .

As shown in Table 4, the CSC ablation study demonstrates its effectiveness. Figure 7 shows that traditional fixed-weight transformations lead to pixel loss in highlight regions during model processing, while our learnable CSC dynamically adjusts channel weight allocation to adapt to complex scenes.

Y

Cb

Cr

Traditional

Learnable

Fig- ure 9 shows that our method effectively removes complex real-world rain, while other methods exhibit varying degrees of rain residue.

Evaluations on other color space . To demonstrate the effectiveness of performing deraining in the YCbCr space, we conduct ablation analysis for different color spaces ( e.g. , RGB, HSV, HSL, YUV, and YCbCr). As shown in Table 4, it is evident that our model (in YCbCr space) achieves the highest results, as nighttime rain is more prominent in the Y channel, which better facilitates deraining.

Effectiveness of network components .

To validate the effectiveness of our network components, we conduct ablation studies in Table 4. All variant models are trained and tested on the HQ-NightRain dataset. The results validate the effectiveness of our components. As shown in the visualization results of Figure 10, the CSC

(a) Rainy input

(b) CSC

(c) IIG

U

(a)

(b)

(c)

(d)

Rainy

input

RGB

Y/Fixed

Y/Learnable

demonstrates excellent rain streak feature extraction capabilities, while the IIG focuses on illuminated regions to guide nighttime deraining.

Extension to multi-weather restoration . We extend our method to multi-weather restoration. Table 6 demonstrates that our method achieves competitive performance on the Muti-Weather6k dataset [29], indicating its potential for multi-weather restoration.

Effectiveness of data synthesis pipeline components on model performance . To evaluate the impact of each component in the data synthesis pipeline on the final model performance, we conduct an analysis using CST-Net, and the results are shown in Table 7. The limited performance of variant D1 indicates that simple linear addition is insufficient for generating realistic images. Our pipeline, by simulating more challenging nighttime scenarios, achieves a balanced improvement in performance.

Extension to natural-scene rain removal . To further validate the effectiveness of our method in nighttime rain removal under natural scenes, we conduct experiments on the Nature subset of the HQ-NightRain dataset. Generalization testing is performed using the model trained on the RS subset, and the quantitative results based on non-reference metrics are shown in Table 5. The proposed method shows consistently better scores across multiple non-reference metrics, indicating enhanced perceptual quality and stronger robustness in complex natural scenes.

Application . To evaluate the applicability of our method to outdoor vision tasks such as object detection, we test it on BDD350-Night [21] using a pre-trained YOLOv8 model. Our method achieves the highest precision, recall, and IoU, showing strong potential for downstream tasks. Beyond denoising, we also apply our data synthesis pipeline to film and game production, generating realistic rainy scenes by modeling illumination effects.

7 Conclusion

In this paper, we rethink nighttime deraining task and propose HQ-NightRain, a high-quality benchmark that reduces the domain gap between synthetic and real data. We also introduce a color space transformation framework to enhance rain removal in the Y channel. Extensive experiments show that our method outperforms state-of-the-art approaches on both synthetic and real benchmarks.

Acknowledgments and Disclosure of Funding

This work was supported in part by the Scientific Research Project of the Education Department of Liaoning Province (No. LJ212410152006).

References

Yuanhao Cai, Hao Bian, Jing Lin, Haoqian Wang, Radu Timofte, and Yulun Zhang. Retinexformer: One-stage retinex-based transformer for low-light image enhancement. In ICCV , pages 12504-12513, 2023.

Wenhui Chang, Hongming Chen, Xin He, Xiang Chen, and Liangduo Shen. Uav-rain1k: A benchmark for raindrop removal from uav aerial imagery. In CVPRW , pages 15-22, 2024.

Chaofeng Chen and Jiadi Mo. IQA-PyTorch: Pytorch toolbox for image quality assessment. [Online]. Available: https://github.com/chaofengc/IQA-PyTorch , 2022.

Sixiang Chen, Tian Ye, Jinbin Bai, Erkang Chen, Jun Shi, and Lei Zhu. Sparse sampling transformer with uncertainty-driven ranking for unified removal of raindrops and rain streaks. In ICCV , pages 13106-13117, 2023.

Xiang Chen, Hao Li, Mingqiang Li, and Jinshan Pan. Learning a sparse transformer network for effective image deraining. In CVPR , pages 5896-5905, 2023.

Xiang Chen, Jinshan Pan, and Jiangxin Dong. Bidirectional multi-scale implicit neural representations for image deraining. In CVPR , pages 25627-25636, 2024.

Xiang Chen, Jinshan Pan, Jiangxin Dong, and Jinhui Tang. Towards unified deep image deraining: A survey and a new benchmark. arXiv preprint arXiv:2310.03535 , 2023.

Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In CVPR , pages 2017-2026, 2022.

Yilong Cheng, Zhijian Wu, Jun Li, and Jianhua Xu. Retinex meets transformer: Bridging illumination and reflectance maps for low-light image enhancement. In ICONIP , pages 388402. Springer, 2023.

Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko. Rethinking coarse-to-fine approach in single image deblurring. In ICCV , pages 4641-4650, 2021.

Xiaofeng Cong, Jie Gui, Jing Zhang, Junming Hou, and Hao Shen. A semi-supervised nighttime dehazing baseline with spatial-frequency aware and realistic brightness constraint. In CVPR , pages 2631-2640, 2024.

Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, and Alois Knoll. Selective frequency network for image restoration. In ICLR , 2023.

P Kingma Diederik. Adam: A method for stochastic optimization. ICLR , 2014.

Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In CVPR , pages 3855-3863, 2017.

Kshitiz Garg and Shree K Nayar. Vision and rain. IJCV , 75:3-27, 2007.

Qiyuan Guan, Qianfeng Yang, Xiang Chen, Tianyu Song, Guiyue Jin, and Jiyu Jin. Weatherbench: A real-world benchmark dataset for all-in-one adverse weather image restoration. arXiv preprint arXiv:2509.11642 , 2025.

Xiaojie Guo and Qiming Hu. Low-light image enhancement via breaking down the darkness. IJCV , 131(1):48-66, 2023.

Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE TIP , 26(2):982-993, 2016.

Yufeng Huang, Xiang Chen, Lei Xu, and Kaiyuan Li. Single image desmoking via attentive generative adversarial network for smoke detection process. Fire Technology , 57(6):3021-3040, 2021.

Quan Huynh-Thu and Mohammed Ghanbari. Scope of validity of psnr in image/video quality assessment. Electronics Letters , 44(13):800-801, 2008.

Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR , pages 8346-8355, 2020.

Yeying Jin, Xin Li, Jiadong Wang, Yan Zhang, and Malu Zhang. Raindrop clarity: A dualfocused dataset for day and night raindrop removal. arXiv preprint arXiv:2407.16957 , 2024.

Chongyi Li, Saeed Anwar, Junhui Hou, Runmin Cong, Chunle Guo, and Wenqi Ren. Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE TIP , 30:4985-5000, 2021.

Hao Li, Xiang Chen, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Foundir: Unleashing million-scale training data to advance foundation models for image restoration. arXiv preprint arXiv:2412.01427 , 2024.

Minghan Li, Xiangyong Cao, Qian Zhao, Lei Zhang, and Deyu Meng. Online rain/snow removal from surveillance videos. IEEE TIP , 30:2029-2044, 2021.

Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, and Deyu Meng. Video rain streak removal by multiscale convolutional sparse coding. In CVPR , pages 6644-6653, 2018.

Siyuan Li, Wenqi Ren, Feng Wang, Iago Breno Araujo, Eric K Tokuda, Roberto Hirata Junior, Roberto M Cesar-Jr, Zhangyang Wang, and Xiaochun Cao. A comprehensive benchmark analysis of single image deraining: Current challenges and future perspectives. IJCV , 129:13011322, 2021.

Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, and Dacheng Tao. Toward realworld single image deraining: A new benchmark and beyond. arXiv preprint arXiv:2206.05514 , 2022.

Yufeng Li, Jiayu Chen, Chuanlong Xie, and Hongming Chen. Rethinking all-in-one adverse weather removal for object detection. SIVP , pages 1-10, 2024.

Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Shunli Zhang, and Robby T Tan. Nightrain: Nighttime video deraining via adaptive-rain-removal and adaptive-correction. In AAAI , volume 38, pages 3378-3385, 2024.

Xin Lin, Jingtong Yue, Sixian Ding, Chao Ren, Chun-Le Guo, and Chongyi Li. Unlocking lowlight-rainy image restoration by pairwise degradation feature vector guidance. arXiv preprint arXiv:2305.03997 , 2023.

Rui Qian, Robby T Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In CVPR , pages 2482-2491, 2018.

Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In CVPR , pages 9147-9156, 2021.

Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR , pages 3937-3946, 2019.

Cidan Shi, Lihuang Fang, Han Wu, Xiaoyu Xian, Yukai Shi, and Liang Lin. Nitedr: Nighttime image de-raining with cross-view sensor cooperative learning for dynamic driving scenes. IEEE TMM , 2024.

Cong Wang, Wei Wang, Chengjin Yu, and Jie Mu. Explore internal and external similarity for single image deraining with graph neural networks. arXiv preprint arXiv:2406.00721 , 2024.

Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In CVPR , pages 3103-3112, 2020.

Xingzheng Wang, Bob Zhang, Zhimin Yang, Haoqian Wang, and David Zhang. Statistical analysis of tongue images for feature extraction and diagnostics. IEEE TIP , 22(12):5336-5347, 2013.

Ye-Tao Wang, Xi-Le Zhao, Tai-Xiang Jiang, Liang-Jian Deng, Yi Chang, and Ting-Zhu Huang. Rain streaks removal for single image via kernel-guided convolutional neural network. IEEE TNNLS , 32(8):3664-3676, 2020.

Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP , 13(4):600-612, 2004.

Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng, and Zongben Xu. Should we encode rain streaks in video as deterministic or stochastic? In ICCV , pages 2516-2525, 2017.

Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE TPAMI , 45(11):12978-12995, 2022.

Qingsen Yan, Yixu Feng, Cheng Zhang, Pei Wang, Peng Wu, Wei Dong, Jinqiu Sun, and Yanning Zhang. You only need one color space: An efficient network for low-light image enhancement. arXiv preprint arXiv:2402.05809 , 2024.

Shuzhou Yang, Moxuan Ding, Yanmin Wu, Zihan Li, and Jian Zhang. Implicit neural representation for cooperative low-light image enhancement. In ICCV , pages 12918-12927, 2023.

Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang. Maniqa: Multi-dimension attention network for no-reference image quality assessment. In CVPR , pages 1191-1200, 2022.

Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR , pages 1357-1366, 2017.

Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE TPAMI , 43(11):4059-4077, 2020.

Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structurepreserving deraining with residue channel prior guidance. In ICCV , pages 4238-4247, 2021.

Zhenqi ang Ying, Haoran Niu, Praful Gupta, Dhruv Mahajan, Deepti Ghadiyaram, and Alan Bovik. From patches to pictures (paq-2-piq): Mapping the perceptual space of picture quality. arXiv preprint arXiv:1912.10088 , 2019.

Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In CVPR , pages 2636-2645, 2020.

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR , pages 5728-5739, 2022.

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, MingHsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR , pages 14821-14831, 2021.

Fan Zhang, Shaodi You, Yu Li, and Ying Fu. Gtav-nightrain: Photometric realistic large-scale dataset for night-time rain streak removal. arXiv preprint arXiv:2210.04708 , 2022.

Fan Zhang, Shaodi You, Yu Li, and Ying Fu. Learning rain location prior for nighttime deraining. In ICCV , pages 13148-13157, 2023.

He Zhang and Vishal M Patel. Density-aware single image de-raining using a multi-stream dense network. In CVPR , pages 695-704, 2018.

Jing Zhang, Yang Cao, Zheng-Jun Zha, and Dacheng Tao. Nighttime dehazing with a synthetic benchmark. In ACM MM , pages 2355-2363, 2020.

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR , pages 586-595, 2018.

Ruikun Zhang, Zhiyuan Yang, and Liyuan Pan. Dehazemamba: large multi-modal model guided single image dehazing via mamba. Visual Intelligence , 3(1):11, 2025.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Relevant content are all summarized.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: The limitations are discussed in Section N of the appendix.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: As shown in Section 4 of the main manuscript and Section B of the appendix. Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: The information required to reproduce our work is provided in Sections I and J of the appendix. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net .

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: The source code and datasets are available at https://github.com/guanqiyuan/CST-Net .

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: Details of training, testing, and hyperparameters are reported in Section 5 of the main paper and Section L of the appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: Our method has almost no randomness, once the random seed is determined. Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: Computing resources are reported in Section 5 of the main paper, and computational complexity is detailed in Section K of the appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We read and obey the NeurIPS Code of Ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We discuss the societal applications of our work in Section 6 of the main paper, including its potential value for downstream object detection tasks and its positive impact on film and game production.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: The paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We cite the related assets and follow their license and terms.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We provide detailed information about the dataset, code, and models in our submission.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: Our method does not involve LLMs.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

A Technical Appendices and Supplementary Material

Overview. The supplementary includes the following sections:

Motivation of the Method. (Section B)

Generation of Initial Rain Streak Masks and Raindrop Masks. (Section C)

Pseudo-code for Dataset Synthesis. (Section D)

Overview of the HQ-NightRain Dataset. (Section E)

Dataset Visualization. (Section F)

Comparison with Existing Datasets. (Section G)

User Study. (Section H)

Details of the Two-Stage Network. (Section I)

Loss Function. (Section J)

Model Complexity. (Section K)

Hyperparameter Validation. (Section L)

Application of Rain Synthesis Technology in Game Production. (Section M)

Limitations. (Section N)

Impact on Downstream Vision Tasks. (Section O)

More Experimental Results. (Section P)

B Motivation of the Method

In this section, we explore the histogram characteristics of different color space channels in daytime and nighttime scenes, as shown in Figures 11 and 12.

In daytime scenes , the histogram difference between rainy and rain-free images in the Y channel is relatively small. This is primarily due to the presence of sufficient illumination, which ensures a high and evenly distributed scene brightness. Natural daylight allows a wide range of brightness levels, resulting in pixel values being more uniformly distributed within the 0-255 range. Consequently, rain streaks have a limited impact on the overall brightness distribution. Additionally, rain streaks mainly cause local contrast variations, which, under well-balanced daylight illumination, do not significantly alter the overall histogram distribution.

In contrast, nighttime scenes exhibit a more pronounced difference between the histograms of rainy and rain-free images in the Y channel. Due to weaker illumination at night, most pixel values are concentrated within a lower brightness range (0-100), while high-brightness areas are restricted to regions illuminated by artificial light sources such as streetlights and vehicle headlights. Under these low-light conditions, rain streaks substantially alter local illumination characteristics, leading to a significant shift in the pixel value distribution of the Y channel. Moreover, the scattering and blurring effects induced by rain streaks under illumination further amplify brightness variations, resulting in a more noticeable distinction between the histograms of rainy and rain-free images. This observation suggests that, in nighttime image processing, the Y channel effectively captures rain streak features, providing a strong foundation for brightness-aware deraining methods.

Beyond the YCbCr color space, the histogram variations in the HSV and LAB color spaces also reflect the impact of rain streaks on different image channels. In the HSV color space , the V channel represents image brightness and exhibits a distribution similar to that of the Y channel. However, due to the nonlinear nature of brightness representation in HSV, its response to rain streaks is less pronounced than that of YCbCr in certain scenarios. In the LAB color space , the L channel also encodes brightness information, but as LAB is designed for perceptual uniformity, its brightness distribution does not highlight rain streak-induced differences as prominently as YCbCr. Overall, among the explored color spaces, the Y channel in YCbCr demonstrates the most distinct response to rain streaks in nighttime scenes, making it a particularly effective choice for nighttime deraining tasks, whereas HSV and LAB show relatively weaker advantages in this regard.

100000

100000

100000

300000

300000

300000

50000

0000S

50000

200000

200000

200000

10

20

30

10

20

30

10

20

30

100000

100000

100000

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

0

100

200

0

100

200

0

100

200

00000

00000

00000

300000

300000

00000

50000

50000

50000

200000

200000

200000

100000

10

20

30

100000

0

10

20

30

100000

0

20

30

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

0

100

200

0

0

100

200

0

0

100

200

100000

100000

00000I

300000

300000

300000

50000

0000S

0000S

200000

200000

200000

0+

0+

10

20

30

20

30

20

30

100000

100000

100000

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

100

200

0

0

100

200

0

100

200

100000

100000

100000

300000

300000

300000

50000

50000

50000

200000

200000

200000

100000

0+

10

20

30

100000

0

10

20

30

100000

10

20

30

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-fre image

Rain-free image

0

0

100

200

0

100

200

0

100

200

C Generation of Initial Rain Streak Masks and Raindrop Masks

Generation of rain streak masks. Rain, as a complex atmospheric phenomenon, is influenced by the combined effects of various natural factors, including raindrop size and density. Fidelity and diversity represent two essential aspects in the process of rain synthesis [7]. Inspired by [15, 39, 7], we model the generation of rain streak layers as a motion blur process, inherently capturing two key characteristics of rain streaks: repeatability and directionality. The mathematical formulation is defined as follows:

where N denotes the rain mask generated by random noise n. We utilize uniform random numbers and thresholding to control the noise level, with l and θ representing the length and angle of the motion blur kernel K ∈ R p × p , respectively. Subsequently, we apply Gaussian blur to introduce a rotated diagonal kernel, controlling the rain width w . Finally, the transparency of the rain is controlled using the function T , generating the final rain mask S . The noise quantity n , rain length l , rain angle θ , and rain thickness w are obtained by sampling from [50 , 200] , [20 , 50] , [ -30 ◦ , 30 ◦ ] , and [3 , 7] .

Generation of raindrop masks. Inspired by work [2], in order to achieve higher quality and more realistic raindrop synthesis images, we use the open-source 3D graphics engine (Blender) to simulate

100000

100000

00000T

300000

300000

300000

0000S

0000S

0000S

200000

200000

200000

+0

10

20

30

0+

10

20

30

10

20

30

100000-

100000

100000

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

100

200

0

0

100

200

0

0

100

200

00000

00000

00000

300000

00000

00000

0000S

50000

0000S

200000

200000

00000

100000

0

10

20

30

100000

0+

10

20

30

100000

0+

10

20

30

Rainy image

Rainyimage

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

100

200

0

0

100

200

0

0

100

200

100000

100000

00000I

300000

300000

300000

50000

0000S

0000S

200000

200000

200000

0

10

20

30

0+

10

20

30

0+

10

20

30

100000

100000

100000

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

M

一

Rain-free image

0

100

200

0

100

200

0

0

100

200

100000

100000

100000

300000

300000

300000

0000S

50000

0000S

200000

200000

200000

100000

10

20

30

100000

0

10

20

30

100000

+0

10

20

30

Rainy image

Rainy image

Rainy image

Rain-free image

Rain-free image

Rain-free image

0

0

100

200

0

100

200

0

0

100

200

and generate real raindrop images. This 3D graphics engine can render raindrops using a physical motion model, allowing us to set depth information and color values separately in the RGB channels [19], facilitated by a Blender plugin called Rain Generator. Inspired by the work in [2], we model the generation of the raindrop layer as a motion blur process. The instantaneous shape of the raindrop at time t is represented by the function r [ t, θ, ϕ ] , where r is the distance from the surface of the droplet to its center, θ is the angle defined as the opposite direction to the point where the droplet falls, and ϕ is the angle between the point and the projection of the line of sight onto any plane perpendicular to the fall direction. The mathematical definition is as follows:

where r 0 represents the undeformed radius of the raindrop, and the factor cos ( mϕ ) depends on the droplet size r 0 . The function p m ( θ ) describes the time-dependent variation of the modal shape and amplitude relative to θ . As the raindrop falls, the effects of aerodynamic forces and surface tension acting on the droplet lead to rapid shape distortions over time.

D Pseudo-code for Dataset Synthesis

We represent the dataset synthesis process using pseudocode, as shown in Algorithm 1.

E Overview of the HQ-NightRain Dataset

Table 8 provides a detailed overview of our proposed HQ-NightRain dataset, including the number of images in each subset.

F Dataset Visualization

In this section, we present additional sample images from the proposed HQ-NightRain dataset in Figure 13. It can be observed that our dataset exhibits greater visual realism and harmony.

G Comparison with Existing Datasets

In this section, we summarize commonly used datasets for daytime and nighttime scenarios, as detailed in Table 9. From the perspective of rain types, the proposed HQ-NightRain dataset offers more

(a) RS

(b) RD

(c) SD

EROS

CRELSEA

11TT

11

(d) Real

comprehensive coverage, providing richer data resources for nighttime deraining tasks. Moreover, as our dataset includes corresponding JSON labels, it enables object detection tasks to evaluate the impact of deraining results on downstream applications. We provide additional visual comparisons with other datasets in Figure 14.

Type

Datasets

Rain Categories

Annotation

RS

RD

SD

Real

Rain200L/H [46]

✔

None

DID/DDN-Data [14, 55]

✔

None

Raindrop [32]

✔

None

Rain13K [21]

✔

None

RainDS [33]

✔

✔

✔

None

MPID [27]

✔

✔

✔

Detection

GTAV-NightRain [53]

✔

None

RoadScene-rain [35]

✔

None

Raindrop Clarity [22]

✔

None

HQ-NightRain (Ours)

✔

✔

✔

✔

Detection

(a) GTAV-NightRain

[53]

(b)

RoadScene-rain

[35]

(c)

HQ-NightRain-

RS (Ours)

(d) Raindrop Clarity

[22]

(e)

HQ-NightRain-

RD (Ours)

STOP

H User Study

In this section, we conduct two user studies. Our survey process is conducted anonymously, with the images in each set randomly shuffled to ensure fairness. The questionnaire is distributed without restrictions to a broad range of online users, and responses are collected from a total of 72 human evaluators. The first focuses on the illumination thresholds τ 1 and τ 2 used to calculate the illumination coefficient matrix I in Equation 4 of the main manuscript. Multiple sets of nighttime rain images are generated using various parameter combinations, and users select the images they perceive as most realistic. As shown in Figure 15, images generated with ( τ 1 , τ 2 ) = (0 . 2 , 0 . 8) are widely preferred, aligning more closely with realistic visual perception. The second user study focuses on subjective evaluations of the realism of different datasets. We randomly selected several groups of nighttime scene images from various datasets [21, 53, 35, 22], and users were asked to choose the most realistic ones. As shown in Figure 16, the HQ-NightRain dataset was deemed more realistic by the majority of human evaluators.

I Details of the Two-Stage Network

Our CST-Net comprises two stages: the degradation removal stage and the color refinement stage. As shown in Figure 17, both stages utilize an identical Transformer-based four-layer encoder-decoder architecture [51]. The expansion ratio of feature channels is set to 2, and the number of modules in the first and second stages is set to { N ′ 1 , N ′ 2 , N ′ 3 , N ′ 4 } and { N ′′ 1 , N ′′ 2 , N ′′ 3 , N ′′ 4 } , respectively. Skip-

(0.1,0.8)

(0.1,0.9)

(0.2,0.7)

(0.2,0.8)

(0.2,0.9)

(0.3,0.8)

0.3

0.28

0.26

0.24

0.22

0.2

0.18

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

Frequency

HQ-NightRain (Ours)

Raindrop Clarity

RoadScene-rain

GTAV-NightRain

0

10

20

30

40

50

N u mb e

r

o

f

S l c i t

n s

connections are incorporated to bridge consecutive intermediate features, enabling stable training. The architectures of the modules used in the network are illustrated in Figure 18. Ablation experiments were conducted to evaluate the impact of different module combinations and the number of modules on the network's performance, with results presented in Table 10.

Table 10: Ablation study for different variants of our method includes the normalization techniques, the number of modules at each stage, and the combination method at each stage.

J Loss Function

In this document, we provide a supplementary introduction to the loss function used. For the degradation removal stage, we utilize the Mean Squared Error (MSE) loss function, defined as follows:

where Y gt represents the Y channel of the ground-truth image, and ¯ Y denotes the predicted result from the degradation removal stage. Additionally, we incorporate Charbonnier [52], Structural Similarity

HW

H.W

H.W

H.W

HW

HxWxC

2~2

××2C

××4C

4~4

××8C

8~8

××4C

4~4

××2C

2~2

HxWxC

Blocks

X N1

×N2

×N3

+NX

×N3

×N2

×N1

(a) Squeeze-Excitation Residual Block (SERB)

(b) Mixed-scale Feed-forward Network (MSFN)

(c)

Multi-Dconv

Head

Transposed

Attention

(MDTA)

(d) Multi-Scale Convolutional Module (MSCM)

0

NI

conv

×

NI

SE

uo

05

05

ReLU

5

ReLU

onv

conv

ReLU

Dconv

ReLU

X

uo

Dcor

R

Norm

K

uo

co1

uo

Dcor

Concat

ReLU

ReLU

ReLU

MaxPool

Cony

Cony

Cony

Conv

3×3

5×5

7×7

1×1

Conv 1x1

(SSIM) loss and edge [10] loss to constrain network training. The Charbonnier loss is defined as follows:

where O RGB denotes the reconstructed image output by the network, I gt represents the ground-truth image and ϵ = 10 -3 is an offset value. The structural similarity loss is defined as follows:

The edge loss is defined as follows:

where W and H represent the width and height of the image, respectively. The proposed loss function L total for network training is defined as follows:

where α is empirically set to 0.5.

K Model Complexity

Table 11 presents the complexity comparison. With a reduced number of modules, our method avoids significant increases in model complexity. Compared to other state-of-the-art methods, our model demonstrates certain advantages.

L Hyperparameter Validation

As shown in Table 12, we validate the illumination threshold hyperparameters τ 1 and τ 2 in Equation 4 of the main manuscript. We empirically test three sets of fixed values and one set of random values. Specifically, we generate datasets using these threshold sets, train them on our CST-Net, and validate them on the RS subset of the HQ-NightRain dataset. The results show that our chosen thresholds (0.2, 0.8) achieve the best performance. Additionally, to evaluate the impact of the thresholds on the dataset's generalization ability, we conduct generalization tests on the real-world dataset RealRain1kL [28]. The results demonstrate that our settings enhance the realism of the dataset and achieve optimal generalization performance.

M Application of Rain Synthesis Technology in Game Production

Besides the application in image deraining, we also show our application in rain synthesis. Our rain synthesis method effectively incorporates the role of illumination, resulting in a more visually realistic effect. Here, we apply this technique to film and game production to simulate rainfall effects. Figure 19 presents one visual example. Compared to expensive rendering engines used during development [53], our synthesis technique also helps reduce production costs.

R.P.D.

(a) Game screenshot

R.P.D.

(b) Rendering of rain scene

N Limitations

Although our method achieves favorable performance, it fails to handle the veiling effect, especially under low-light conditions where this effect interacts with artificial light sources. Future work will explore the incorporation of physical models to address this issue.

O Impact on Downstream Vision Tasks

To investigate the impact of nighttime image deraining on downstream visual tasks, such as object detection, we evaluate on the BDD350-Night [21] dataset using YOLOv8. As presented in Table 13, our results achieve the highest values in Precision, Recall, and IoU across three metrics. As shown in Figures 20 and 21, our deraining results yield higher recognition accuracy, demonstrating that CST-Net effectively enhances subsequent detection performance. Thus, our method has greater potential for application in downstream vision tasks.

(b) PReNet [34]

(c) RCDNet [37]

(d) Restormer [51]

(e) SFNet [12]

(f) DRSformer [5]

(g) NeRD-Rain [6]

(h) CST-Net (Ours)

(b) PReNet [34]

(c) RCDNet [37]

(d) Restormer [51]

(e) SFNet [12]

(f) DRSformer [5]

(g) NeRD-Rain [6]

(h) CST-Net (Ours)

809

car

car

car

160978

car

car

car

P More Experimental Results

In this section, we present additional visual comparison results. Figures 22 and 23 show the visual comparisons on the synthetic dataset, GTAV-NightRain [53]. Compared with other methods, our CST-Net generates high-quality deraining results with more accurate detail and color restoration. Figures 24-26 illustrate the visual results on the real-world RainDS-real [33] dataset, including its three subsets: rain streaks (RS), raindrops (RD), and rain streaks mixed with raindrops (RSD). Our method effectively removes complex and random rain streaks and raindrops, achieving visually satisfactory restoration.

(b) PReNet [34]

(c) RCDNet [37]

(d) SPDNet [48]

(e) IDT [42]

(f) Restormer [51]

(g) SFNet [12]

(h) DRSformer [5]

(i) RLP [54]

(j) MSGNN [36]

(k) NeRD-Rain [6]

(l) CST-Net (Ours)

onalOf

onalCm

cuity

cuity

curity

rcem

onalO

uity

cunity

uTt

(b) PReNet [34]

(c) RCDNet [37]

(d) SPDNet [48]

(e) IDT [42]

(f) Restormer [51]

(g) SFNet [12]

(h) DRSformer [5]

(i) RLP [54]

(j) MSGNN [36]

(k) NeRD-Rain [6]

(l) CST-Net (Ours)

(b) PReNet [34]

(c) RCDNet [37]

(d) MPRNet [52]

(e) IDT [42]

(f) Restormer [51]

(g) SFNet [12]

(h) DRSformer [5]

(i) RLP [54]

(j) MSGNN [36]

(k) NeRD-Rain [6]

(l) CST-Net (Ours)

(b) PReNet [34]

(c) RCDNet [37]

(d) MPRNet [52]

(e) IDT [42]

(f) Restormer [51]

(g) SFNet [12]

(h) DRSformer [5]

(i) RLP [54]

(j) MSGNN [36]

(k) NeRD-Rain [6]

(l) CST-Net (Ours)

(b) PReNet [34]

(c) RCDNet [37]

(d) MPRNet [52]

(e) IDT [42]

(f) Restormer [51]

(g) SFNet [12]

(h) DRSformer [5]

(i) RLP [54]

(j) MSGNN [36]

(k) NeRD-Rain [6]

(l) CST-Net (Ours)

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the task of nighttime image deraining and introduces a high-quality benchmark dataset HQ-NightRain, which takes into account the impact of night illumination on rain imaging, improves the realism and reduces the domain gap compared with existing datasets. In addition, the article also proposes a learnable color space transformation network (CST-Net). Based on the observation that night rain is more obvious in the Y channel of the YCbCr color space, the YCbCr color space is used for deraining. Extensive evaluations on both synthetic and real datasets demonstrate its excellent performance and generalization ability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1.  The paper is logically clear and easy to understand, effectively conveying the research.\n2.  The HQ-NightRain dataset solves a key problem of nighttime rain removal by considering the effect of illumination on rain imaging, narrowing the domain gap with real data and contributing to the image rain removal community.\n3.  Extensive experiments demonstrate that CST-Net outperforms existing solutions on multiple datasets, showing its superior performance in nighttime rain removal. It also shows strong potential in downstream tasks, such as object detection under nighttime rainy conditions, extending its use to more than just rain removal.\n\nWeaknesses：\n1. Although the architecture of CST-Net is outlined in the paper, it lacks some implementation details.\n2. The proposed method verifies its effectiveness in night scenes. Is it also beneficial in daytime scenes? Can it also effectively remove rain?\n3. In the experiments, the Mamba-based image deraining method is missing from the baseline comparisons. It is recommended that the authors include the Mamba-based method to improve the completeness of the benchmark evaluation.\n\nQUESTIONS:\n1. How is the rain mask, which includes both rain streaks and raindrop patterns, generated?\n2. It would be helpful if the defocus blur operation used could be explained in more detail. Does this operation rely on the nighttime lighting distribution of the entire image?\n\nLIMITATIONS:\nYes, the authors have discussed the related limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the problem of removing rain from nighttime images. Compared with removing rain during the day, the research on nighttime scenes is more challenging. The contributions of this paper include two aspects. First, it constructs a new dataset HQ-NightRain. On the other hand, it proposes a new image quality restoration framework, color space transformation framework (CST-Net). The method proposed in the paper utilizes the transformation of color space.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n(1) This paper constructs a new dataset HQ-NightRain, which is very helpful for night rain removal tasks.\n(2) This paper proposes a new image quality restoration framework color space transformation framework (CST-Net). This framework utilizes the transformation of color space. The paper explains why this transformation is used.\n(3) The dataset proposed in the paper uses a reliable night rain streak imaging model.\nWeaknesses：\n(1) The use of image restoration technology based on color space transformation is not a very novel strategy.\n(2) The deraining network proposed in the paper is mainly introduced in the supplementary materials. The network architecture and modules used are relatively common. For example, Squeeze-Excitation Residual Block (SERB) and so on.\n(3) The loss function used in the paper is also relatively common. This makes the paper lack necessary innovations in network design and training process.\n\nQUESTIONS:\nAs for this study, I think it is very interesting overall. The paper not only creates a dataset, which is very helpful for nighttime rain removal research. In addition, the paper also proposes a corresponding network based on color space transformation. Some of my doubts are as follows:\n(1) Image restoration technology based on color space transformation has been explored in many papers. For example: Guided Real Image Dehazing using YCbCr Color Space.\n(2) The proposed network structure and loss function are relatively common according to the supplementary materials. I don't know if I understand it wrongly. The network includes: Squeeze-Excitation Residual Block (SERB), Mixed-scale Feed-forward Network (MSFN, Multi-Dconv Head Transposed Attention (MDTA) and (d) Multi-Scale Convolutional Module (MSCM). This is the main factor for my score.\n(3) t-SNE distribution visualization shows the distribution relationship of different datasets. Does this figure show that the proposed nighttime dataset is closer to the real world? How to prove this? I don’t see any overlap in the data points of the proposed dataset and the real world dataset. There is no overlap between the two distribution clusters. In t-SNE, does the distance between different distributions represent the size of their distribution differences?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work rethinks nighttime image deraining by addressing key issues in both data and methodology. The authors first introduce HQ-NightRain, a new high-quality dataset that generates more realistic rainy scenes by modeling the dependency of rain visibility on local illumination. To leverage this, they also propose CST-Net, a novel framework that uses a learnable color space converter to perform deraining in the more effective Y (luminance) channel. The proposed method achieves state-of-the-art results on several synthetic and real-world benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrength：\nDataset Contribution: The introduction of the HQ-NightRain dataset is a significant contribution, featuring an illumination-aware synthesis pipeline that addresses a key limitation in prior work by generating more physically-grounded, non-uniform rain. \nModel Framework Innovation:The methodological novelty of the CST-Net framework centers on its learnable color space converter (CSC), which moves beyond fixed transformations to adaptively learn an optimal feature space for the deraining task. \nExcellent Performance: The proposed method demonstrates state-of-the-art (SOTA) performance on multiple synthetic and real-world benchmarks.\nWeaknesses：\nReliance on Fixed Hyperparameters: The dataset construction relies on fixed illumination thresholds. This one-size-fits-all approach may be sub-optimal for synthesizing images from backgrounds with diverse lighting conditions (e.g., a dimly lit street vs. a bright downtown area), potentially limiting the dataset's robustness and generalization.\nSimplifications in the Synthesis Pipeline: While the illumination-aware concept is novel, the synthesis pipeline involves certain simplifications. The use of fixed illumination thresholds and the modeling of raindrops solely through defocus blur are approximations that do not fully capture the complexity of real-world optics, suggesting that the dataset's realism could be further enhanced.\nLimited Scope to Single Degradation: The work's focus is narrowly on deraining and does not address the common challenge of mixed adverse conditions. Real-world nighttime scenes often feature rain combined with fog or haze, and the paper provides no analysis of how robust the proposed method—particularly its core reliance on the Y-channel—would be in these more complex, multi-degradation scenarios.\n\nQUESTIONS:\n1. The data synthesis pipeline uses a 3x3 convolution, f[·], to merge the background and rain masks. Could you provide more justification for this specific choice over simpler methods like linear addition or alternative network structures? It would be valuable to understand if an ablation study was performed to assess the sensitivity of the final dataset's quality to this merge strategy.\n2. The paper effectively demonstrates the proposed dataset's superiority through generalization experiments (Table 3), which evaluates the synthesis pipeline holistically. Was any analysis done to isolate the contributions of individual components within the pipeline? For instance, what is the quantitative impact of the illumination-aware blending (σ(·)) versus the defocus blur (ρ(·)) on the final model's performance?\n3. The architecture's central premise—that rain artifacts are overwhelmingly present in the Y-channel—is challenged by scenarios involving colored rain streaks. Could you comment on how the model performs in such cases? Specifically, does passing the noisy Cb/Cr channels directly to the refinement stage compromise its ability to perform accurate color correction and potentially lead to color artifacts in the final output?\n\nLIMITATIONS:\n1. The dataset synthesis pipeline, while innovative, relies on certain simplifications. It uses fixed illumination thresholds, which may not be optimal for all lighting scenarios, and models complex raindrop optics solely with defocus blur.\n2. The work's scope is focused narrowly on deraining and does not address the common real-world challenge of mixed adverse conditions (e.g., rain with fog), where the robustness of the Y-channel-centric approach remains unevaluated.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a high-quality benchmark dataset, **HQ-NightRain**, for nighttime image deraining, enhancing the realism of synthetic images. It proposes a robust, learnable **color space transformation** framework that leverages the **Y channel** for improved rain removal. Experimental results show that the method outperforms existing approaches, demonstrating the effectiveness of both the dataset and the proposed model.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. HQ-NightRain considers the visibility of rain under varying illumination conditions by using illumination coefficient matrix. This design reduces the domain gap between synthetic and real-world rainy videos.\n\n2. This paper proposes CST-Net, an effective color space transformation framework CST-Net for nighttime image deraining.\n\n3. The paper is well written. Figures 1–4 and all equations clearly illustrate the main motivation and contributions.\n\n4. Experiments are comprehensive and results are solid. This paper provides experiemntal results on multiple benchmarks, including HQ-NightRain, public and real-world datasets. \n\n### Weaknesses\n1. Future work. This paper demonstrates the effectiveness of CST-Net for image deraining by operating in the Y channel. Can this approach be extended to video deraining tasks? Is it also adaptable to snow removal?\n\nQUESTIONS:\n### Major issues\n1. About Fig. 1.\n    - Are the histogram statistics based on real or synthetic nighttime rainy images?\n    - Is the histogram computed from a single image pair (with and without rain)? If so, please provide the corresponding image. If it is based on multiple images (as expected for a histogram), please show examples of both rainy and clean images. Since the Y channel mainly reflects brightness, the results could be influenced by the lighting conditions in nighttime scenes.\n\n\n### Minor issues\n1. On page 2, line 42, the paper states that existing methods remove rain in the RGB color space rather than the YCbCr space. However, some prior works have already explored deraining in the YCbCr color space as shown in their publicly available code. Please cite them [1-3] appropriately.\n\n### References\n\n[1] Wei, Wei, et al. \"Should we encode rain streaks in video as deterministic or stochastic?.\" Proceedings of the IEEE International Conference on Computer Vision. 2017.\n\n[2] Li, Minghan, et al. \"Video rain streak removal by multiscale convolutional sparse coding.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.\n\n[3] Li, Minghan, et al. \"Online rain/snow removal from surveillance videos.\" IEEE Transactions on Image Processing 30 (2021): 2029-2044.\n\nLIMITATIONS:\n***Future work:*** This paper demonstrates the effectiveness of CST-Net for image deraining by operating in the Y channel. Can this approach be extended to video deraining tasks? Is it also adaptable to snow removal?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}