Rethinking Nighttime Image Deraining via Learnable Color Space Transformation
Authors: Qiyuan Guan
Xiang Chen
Guiyue Jin
Jiyu Jin
Shumin Fan
Tianyu Song
Jinshan Pan
Venue: NeurIPS 2025 poster ()
Keywords: Nighttime image deraining
Benchmark dataset
Color space transformation
Primary area: applications

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
Qiyuan Guan" Dalian Polytechnic University gyuanguan@gmail. com
Xiang Chen" Nanjing University of Science and Technology chenxiangen just edu cn
Guiyue Jin
Dalian Polytechnic University guiyue jin@dlpu. edu. cn
Jiyu Jin'
Shumin Fan Dalian Martime University shuminf an@163. com
Tianyu Song Dalian Martime University songtienyu@163.cm com
Dalian Polytechnic University jiyu.j joyuu.i@dppp.cco edu. edu. en
Jinshan Pan
Nanjing University of Science and Technology jspandnjust. www.:uutttddd.....
Abstract
Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack 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 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 learnable color space converter (CSC) to better facilitate rain removal in the 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 https//ggihub..omuuuuqauuulaa/
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, is of great interest develop effective algorithm to recover high-quality rain-free images from nighttime scenarios. Recently, several efforts [35, 22, 7, 24, 16] have been made address nighttime image deraining, with the emergence of some synthetic datasets, including GTAV Nigh--NigtRRain [53] and Raindrop Clarity [30]. However, we note that most existing synthetic datasets [2, 46, 21] feature global uniform distribution of rain, as random rain masks are added linearly to nighttime backgrounds. This naturally leads to
"Co-first authorship 'Coreeponniig author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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380003
700093
300003
100004
30000
200000
200030
203030
100000
100000
100090
100000
Y (YCbCr); Nighttime (b) (YCbCr); Daytime (c) V (HSV); Nighttime
L (LAB); Nighttime
Figure/ 1: Histograms of different color channels show that the Y channel in the YCbCr color space demonstrates the most significant difference between rainy and rain-free images at nighttime.
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 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 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 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 exploring 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- rain-free images more prominent in the Y channel compared to the RGB channels. Therefore, this motivates us develop an effective approach performed in the Y channel, tailored for nighttime image deraining.
To this end, propose a 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- tfth-ot ones on our proposed dataset and public benchmarks. The main contributions are summarized as follows:
effective color space transformation framework CST-Net for nighttime
We construct high-quality nighttime image deraining benchmark dataset HQ-NightRaid which further improves the harmony and realism of synthetic images.
We propose robust learnable color space transformation framework for nighttime image deraining, which explores the potential of the channel in rain removal.
We demonstrate the effectiveness of our dataset, and show that our proposed method achieves favorable deraining performance against state-of-the -hetttrrrt 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 25, 26, 41]. We note

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Rain streck mack (R5)
Rahh STredk
Rosn streck mark (R3x)
streak image (Chee)
Stort
Bon Loaers
Rain stresk image (Cho)
Ceefficin Mormi
Bockground (B)
Pwiby
Aulidinp waak (8D)
Randrra image (Dis)
Romohrop rock (lbb)
(4Dc)
Ramdirp image (Des)
Previous
method
Figure 2: Data construction pipeline of previous method and our proposed approach. Existing methods add global rain effects linearly onto nighttime backgrounds to generate rainy images. Insetad, our approach takes illumination information in nighttime scenes into account to synthesize nighttime rainy images with higher harmony and realism.
existing image deraining efforts primarily focus on daytime scenarios [47]. with comparatively little attention given nighttime deraining. For the nighttime deraining dataset, Zhang el al. [53] first proposed the GTAV-NightRain dataset for nighttime rain streak removal, using ray tracing technology gaming platform render rainy scenes. Li es 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 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 large domain gap between synthetic and real images. Instead, we build nigh-quality benchmark by taking into account illumination properties of nighttime images.
For the nighttime deraining method, Zhang et al. [54] proposed rain location prior (RLP) that employs recurrent residual network to learn the positional information rain streaks from nighttime rainy images. Lin al. [30] developed teacher-studen framework with 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 learnable color space transformation to perform rain removal in the YCbCr color space.
Color space transformation. Color space transformation refers the process of converting an image from one color space to another to facilitate various image processing tasks. As result, color spaces like YCbCr, HSV, HVI, etc., have been explored 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). nighttime rainy images, rain often more pronounced the channel compared to the RGB channels. Nighttime rain can significantly affect the overall brightness, making them more visible in the channel [17]. 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 illumination- fused 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, and raindrop images R.g. Mathematically, it can be expressed as:
R, = S. Ra = (1 M) の B
(I)
where represents the rain-free nighttime background, S is the rain streak mask, D is the raindrop mask, M is a binary mask, and @ denotes clement-wise multiplication.

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40-
Raindlop Clarity
20-
Rairsk
RowiSeene-rrin
GTAV-NiaMRain
HQ-Rain
Laop Gee!
UA--alnik
30
Red werhd Rainn
RQ-NightRain (Ours)
Real-world Nighttimo Imagu
-30
200
11
10
20
30
10
(a) Comparison of different nighttime rainy visual samples
(b) Distributions of t-SNE
Figure 3: (a) Sample images from different datasets. Our dataset carefully considers harmony in synthesizing nighttime rainy scenes, resulting in more visually realistic appearances. (b) To avoid the influence of background content on the results, select samples with similar nighttime street backgrounds and use ResNet50 to extract features. Images from the same dataset exhibit clustering after t-SNE dimensionality reduction due the similarity of rain streak features. The small domain gap indicates that our dataset synthesis pipeline generates nighttime rain that is closer to reality.
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 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 surface 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 new rain model for nighttime rain imaging, defined as:
R₂ = (S)), R4 = - M) o pl B), o (D)).
where the function o--) incorporates illumination information into the rain streak and raindrop masks, [[]]] represents the 3x3 convolution function used to merge the background with rain mask, and p(-) 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 non- uniformly distributed based 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 defined as:
N = Norm (Fo (TRGE2HSV (B))),
where N denotes the initial illumination matrix, denotes the color space transformation, fo denotes the extraction of the V channel, and Norm is the normalization function.
Rain is not only invisible in 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 visible. The final illumination coefficient matrix is expressed as follows:
areas but also in excessively nighiillumination areas,
v/2, if(N (ma)) T1 || N(r.w) 2 72)
Mask(N) = -{
U, other
where is the value at position (x, in matrix N, тi and 72 are denote the illumination thresholds.

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lcr
cco
JRGB
1y
csc
CSC
RGB
Rainy input (RRGs)
IIG
Output (ORce)
Coliee lorin For Vale
RGB Coler Spacd
Learable
Ilurrination Intendity Romn Dereity
Local focusing
MLP
1g
Color Space Converter (CSC)
Implicit Illumination Guidance (II6)
begrodation Removal
Color Refirement Stage
RSB to YCSC+ Tratal orriction
YOuC- to REe
Element wide Traneformation
Addittion
Element-ve Concotenation
Figure 4: Overall architecture of the proposed end-to-end CST-Net for nighttime image deraining. which consists of a degradation removal stage and color refinement stage. Among these two stages, we develop a color space converter (CSC) to achieve space transformation (RGB = YCbCr), and also construct an implicit illumination guidance (IIG) branch to better transmit illumination information.
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:
S o(D) = DOL
(5)
where (-) 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 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-NightRan are selected from the public BDD100K [50] dataset, which contains images captured from first-person driving scenes urban environments. Here, 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 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 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 nightrime rainy scenes, which reduces the domain gap between synthetic and real images. We additionally provide a real-captured subset comprising 512 images and synthetic nighttime rain subset in natural scenes comprising 20 image pairs.
Proposed Method
4.1 Overall Pipeline
To better model the complex rain information in nighttime images within the channel, we develop 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 the color channels (e.g., RGB), we first perform Y-channel based image deraining in the degradation removal stage. Specifically, given a rainy image T JRGB H x3 where and RGB
W denote the image height and width, implement color space transformation to facilitate the
5

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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 into three channels: ly IR fl xX y
Ics CyCr #xxWx1 and for R xfxxwx CbCr Next, we feed Iy into the degradation removal stage to perform rain removal in the luminance channel, while the chrominance channels Icb and Ic, are directed the color refinement stage. This process defined as follows:
ly, Icb, Icr sppit((SCRcc--yocc
1y Pi(Iy),
(6)
where split means splitting the channels, P1 denotes the first stage, and 1y E ByCtCr 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 Iy E H x x = IIG( 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:
CbCCr
I' RGGGB CSC CCCy Jcb.
ORGB Pa(Ly. (JRGB + I' IGRR))),
(7)
where I' RGB E R RGB cat[-] represents element-wise concatenation, P2 denotes the second stage, and ORGB is the final derained image.
#xxxx3 represents the output from the first stage converted back to the RGB space,
4.2 Learnable Color Space Converter
As 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 follows:
0.299
0.587
0.114
R
Ch
=
0. 169
0..31
0.5
o
G
(8)
Cr
0.5
-0.419
-0.081
B
where denotes matrix multiplication.
However, this fixed color space conversion is based on standard paradigm for general scenes. 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 learnable matrix & with shape of Unlike the fixed-weight linear transformation in W, our proposed CSC has no fixed linear weight matrix. Instead, each parameter is replaced by learnable one-dimensional variable, enabling more flexibility in the transformation. This matrix is defined as follows:
P1,11
P1,2
11,3
(pI.g
22,1
P2,,
|2,3
[P3,1
P3,2
$3.33
where Piy 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 semi- transparent 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 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 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:
R MLP
Cb
④)) o
(10)
Cr
B
where MLP(-) denotes the operation performed by the MLP.

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Table I: Quantitative evaluations on the HQ-NightRain dataset and GTAV-NightRain [53] dataset. The best and second-best values are blod and underlined.
HQ-NightRain
Datasets
GTAV-NighRRiin
Average
RS
Methods
PSNR
SSIM
LPIPS
PSNR
SSIM LPIPS PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNK
SSIM
LPIPS
PReNet
38.5849
0.9842 0.0234
32,0029 0.9432 0.1709 34,8573 0,9698 0.0730
36.6352
0.9703 0,0609
35,5146
0,9669 0.0821
RCDNet
37.7537 0.9788 0.0357
31,8933
0.9357 0.1842 32.7796 0,9512 0.1452
37,0809
0,9703 0.0618
34,8766
0,9590 0.1072
SPDNet
40,0493
0.9865 0.0188
31,4772
0,9361 0. 0.1638 39,0873 0,9825 0.0405
38.0175
0.9748 0,0462
1453
0,9700 0.0673
IDT
42,4204
0.9918 0.0116 33.6176
0.9522 0,1350 38.4873 0,9843 0.0361
37,5592
0,9744 0,0493
38.0211
0,9757 0.0580
Restormer
41,8844
0.9907 0.0145
33.7958
0,9503 0. 1289 40.1790 0,9884 0.0266
18.1
0:9772 0.0403
38.4966 0.9767 0.0526
SFNet
41,4805
0.9920 0.0139
33.6059
0.9465 0.1268 40.3011 0,9875 0.0243
37,5404
0.9738 0.0470
38.2320 0,9749 0.0530
DRStomer
42,8107 0,9922 0.0126
33.8452
0.9491 0.1348 40,4315 0,9886 0.0251
37,8722
0.9766 0,0415
38. 38.7399 0,9766 0.0535
RI.P
40.4093 0.9885 0.0167
29.9728
0.9204 0. 0.44 31.1297 0.9709 0.0855
14,90
0.9600 0.0945
34, 1185 0,9600 0.0928
MSGNN
27.7182 0.8846 0.2429
24.5151
0.8244 0,4946 27 73,3339 0,9078 0.2884
34.7993 0.9562 0.1 180
28. 6666 0.8933 0.2860
NeRD Rain
42.7139
0.9923 0.0109
33.8313
0.9500 0.1391 39.6834 0,9855 0.0320
37. 37.81337
0.9738 0.0530
38.5106 0.9754 0.0588
CST Net (Ours)
42.8850 0.9924 0.0100)
33.9395
0.9523 0.1239 40.4984 0,9881 0.0248
38.93778 0.9786 0.0320
39.0652 0.9778 0.0477
Table 2: Quantitative evaluations on the RealRain- [28] dataset and RainDS-real [33] dataset.
RealRain-Ik
RainDS-real
Datasets
RealRain- Ik-1.
RealRain-II-F
RD
Methods
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNK
SSIM
LPIPS
PReNet
27.1939 0.8881 0.3941
23.4536
0,7977 0.5036 23.0181 0.6857 0.3267
19.5145
0.6270 0,3980
18. 18.7119
0,5900 0.4454
RCDNet
27.1157 0.8862 0.3965
23.4234
0,7964 0,5050 23.6687 0.6763 0.3540
21.5567
0.6246 0.4106
20.6816
0,5859 0.4615
MPRNet
27.1221
0.8867 0,4007
23,5270
0,7933 0,5097 23,9263 0,6872 0.3231
21,9558
0.6339 0.3831
21,0407
0,5972 0.4338
IDT
26.9428 0.8873 0.3912
23.44992
0,7997 0.4977 24.1806 0.7088 0.2950
21,8945
0.6551 0.3635
21.0991
0.6219 0.4921
SINNet
26.7338 0.8861 0.3912
23.21336
0.7984 0,4964 24,4064 0.6971 0.3000
22,0831
0.6510 0.3521
21.0251
0.6095 0.4159
DRSfomer
27,2100 0,8885
0.3932
23,7299
0,8049 0.4970 24.809 0.7052 0,2833
21.7949
0.6415 0,3658
20.7358
0.6040 0.4046
RLP
26.8646
0.4026
23.1733
0.7898 0.5119 22.7828 0.6601 0.3411
20.5325
0.6136 0,3909
19.6163
0.5694 0.4521
MSGNN
25.5384
0.8692
0.4337
22.0136
0,7702 0.5354 22.7039 0.6572 0.3427
19. 3444446
0.6168 0.3735
18. 2088 0.5637 0.4476
NeRD-Rain
27.1613
0.8895
0.3867
23.6547
0.8046 0.4915 24.2879 0.6870 0.2912
22.0 22.0290 0.6329 0.3523
21.1
0.5943 0.4095
Net (Ours)
27.3066 0.8891
0.3805
23.8114
0.8062
0.4877 25.0456
0.7065
0.2715
22.7280
0.6499 0.3479
22,0070 0.6175 0.3816
4.3 Implicit Illumination Guidance
To capture illumination information guide the nighttime rain removal, we introduce 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 Iy" stored in the corresponding coordinate set P E R. x 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:
P P(w.w)
Σ
w(t' (y
(11)
(x' (E(,)))(().))))
where P(a.y) represents the local illumination information at the central coordinate (I, y). while M(I, y) denotes the neighborhood region centered around (I, y); w(a', v/) 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(awy)- then feed them into the MLP as a decoding function to obtain the final output image Iy. which is defined as:
1& MLP [E, P(z.w))),
(12)
where the MLP is adopted to map coordinates to their predicted 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 ML.. Diverse illumination distributions yield different sets of parameters, which in turn means the MLP is adaptive to the complex nighttime rainy scenes.
7

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(a) Rainy input
(b) Ground truth
(c) IDT
(d) Restormer
(e) DRSformer
NcRD-Rain
(g) Ours
Figure 5: Derained results on the HQ-NightRain (first row) and GTAV-NighRRain [53] (second row).
(a) Rainy impui
RCDNet
(c) IDT
Restormer
(e) DRSformer
NeRD-Rain
(g) Ours
Figure Derained results on the real-world nighttime rainy image from the RealRain- [28] dataset.
5 Experiments
5.1 Experimental Settings
Datasets and metrics. We conduct the experiments deraining dataset GTAV-NightRain set1 [53], and two real-world benchmarks (e.g., RealRain-Ik [28] and RainDS- real [33]). To evaluate the quality of each derained image, use PSNR [20, 3], SSIM [40], LPIPS [57], PaQ-2-PiQ [49], and MANIQA [45] as evaluation metrics.
HQ-NightRain dataset, public nighttime
Implementation details. In CST-Net, both the degradation removal stage and the color refinement stage adopt 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 x 10-4 and gradually reduces to 1 x 10-6 using a cosine annealing scheduler. The model is trained for 500 epochs with patch size of 128 128 pixels and batch size of 4. We set the illumination thresholds hi and 72 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]. SFNett [12], DRSformer [5], RLP [54], MSGNN [36], and NeRD-Rain [6].
Evaluations on the HQ-NightRain dataset. Table reports the quantitative results of different approaches on the HQ-NightRain dataset. All methods are retrained the proposed dataset for fair comparison. 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-Rait Rain [6] by 0.81dB PSNR on the SD subset. In the first row of Figure 5, further show the visual comparison. Compared to existing methods that still leave residual rain, our method can restore clearer results.
Evaluations public datasets. Table summarizes the quantitative results the public GTAV- NightRain 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

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Table 4: Ablation analysis of different variants in method, including two-stage network pipeline, other color space transformation (RGB, HSV, HSL, YUV and YCbCr), learnable color space converter (CSC), and implicit illumination guidance (IIG).
Methous
Stagel
Network Pipelne
Stagez
RGH
Other Color Space Transformation
HNV
HSI.
YUV
VCEC.
LSC
Learnable
IIG
PSNK
Metrics
SSIM
Our suacller vCtcche dscawi
3500858
D.9650
Oursuh supattte
X
X
36.4385
0.9740
Outsu Sagel Vasche escatwo
K
X
39.5767
0.9856
Outtu Sugsi= taw arisd ESCWoo
X
X
X
X
39:0317
D.9843
Ours Suget
V
39. 1613
0.9844
Oursut Sugris CSS CSCa
Ours, Shgel-2lww VClCheeaa CSCaswli
Ours
X
X
40.4984
0.9881
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 clear background.
Evaluations on real-world datasets. We also evaluate our method on the challenging RealRain- Ik [28] and RainDS-reat datasets [33]. Quantitative results Table 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 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.
Table 3: Performance comparison of
To demonstrate that the model trained on our HQ- NightRain dataset generalizes better to real-world night- time 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 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 syn- thetic and real-world nighttime rainy scenes.
other method (e.g., IDT [42]) train dif- ferent synthetic nighttime image derain- ing datasets and test on the real-world dataset RealRain-Ik-L [28].
Training Datasets
PSNR SSIM
GTAV-NightRain
26.47 0.8640
RoadScene-rain
25.63 / 0.8408
HQ-NightRain (Ours)
26.94 0.8873
Effectiveness of the learnable CSC.
Lombie Inderre
As shown in Table 4, the CSC ablation study demon- strates its effectiveness. Figure 7 shows that tra- ditional fixed-weight transformations lead pixel loss in highlight regions during model processing, while our learnable CSC dynamically adjusts channel weight allocation to adapt to complex scenes. Fig-
Figure 7: Visualization of intermediate fea- tures in the degradation removal stage. ure 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 com- ponents, 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. the visualization results of Figure 10, the CSC
(e) Ramy imput
(b) CSC
(C) IIG
shown in
Figure 10: Feature visualization from network components.

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Table 5:
Performance parison of nighttime deraining in natural scenes using non- reference metrics.
b
Methods
PaQ-2-PIQ/ MANIQA
0.5505
Figure 8: Generalization results on real-world nighttime rainy images trained on different nighttime image deraining datasets. Real rainy input (a), and results from models trained on GTAV-
Restormer
63.7980/ 0.5500
MSGNN
56.8076/ 0.4483
CST-Net (Ours)
63.8756/ 0.5079
NightRain (b), RoadScene-rad (c), and HQ-NightRain (d).
Table 6: Performance compar-
ison of multi-weather restora- tion on the Multi- Weather6K
dataset [29].
Methods
PSNR SSIM
Rainy nnpu
Y.F
Restormer
3,922
Figure 9:
Visual comparisons real-world nighttime rainy images. 'RGB' denotes processing in the RGB color space, "Y' denotes processing in the Y channel, 'Fixed' 'Learnable' repre- sent CSC with fixed weights and CSC with learnable weights.
Trans Weather
30.75 / 0,9468
PromptIR
31.69 0.9169
CST-Net (Ours)
33.82 0.9642
demonstrates excellent rain streak feature extraction capabilities, while the IIG focuses on illuminated regions to guide nighttime deraining.
Extension to weathr restoration. We extend our method to weather restoration. Table 6 demonstrates that method achieves competitive performance the Muti-Weathes Weatherók dataset [29], indicating its potential for multi-weather restoration.
Effectiveness of data synthesis pipeline com- ponents on model performance. To evaluate the impact of each component in the data syn- thesis pipeline on the final model performance, we conduct analysis using CST-Net, and the results are shown Table 7. The limited perfor- mance of variant DI indicates that simple linear addition is insufficient for generating realistic images. Our pipeline, by simulating more chal- lenging nighttime scenarios, achieves a balanced improvement in performance.
Table 7: Impact of dataset synthesis pipeline components on model performance, where Lin- ear denotes linear addition, f denotes convo- lutional merge, o o1-] denotes illumination merge, and p(-] denotes defocus blur.
Variants Linear
SH]
of
p
PSNR
SSIM
D1
X
x
x
25.38
0.8705
D2
X
V
x
x
28.58
0.9456
D3
x
v
x
36.55
0.9754
Ours
x
31.91 0.9493
Extension natural-scene rain removal. To further validate the effectiveness of our method in nighttime rain removal under natural scenes, we conduct experiments 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 BDD335-Night [21] using a pre-trained YOLOv8 model. Our method achieves the highest precision, recall, and loU, 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 bench- mark that reduces the domain gap between synthetic and real data. We also introduce color space transformation framework to enhance rain removal in the Y channel. Extensive experiments show that our method outperforms oftte-of-hh-attt approaches on both synthetic and real benchmarks.
10

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Acknowledgments and Disclosure of Funding
This work was supported in part by the Scientific Research Project of the Education Department of Liaoning Province (No. L1212410152006),
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A Technical Appendices and Supplementary Material
Overview. The supplementary includes the following sections:
Motivation of the Method. (Section B)
Generation 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
Model Complexity. (Section K)
Hyperparameter Validation. (Section L)
Application of Rain Synthesis Technology 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 channel is relatively small. This is primarily due to the presence of sufficient illumination, which ensures high and evenly distributed scene brightness. Natural daylight allows a wide range of brightness levels, resulting 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 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 lower brightness range (0-100), while high-brightness areas 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 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 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 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 distribution similar that of the Y channel. However, due to the nonlinear nature of brightness representation in HSV, its response to rain streaks less pronounced than that of YCbCr in certain scenarios. In the LAB color space, the I. 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 YCbCr. Overall, among the explored color spaces, the Y channel in YCbCr demonstrates the most distinct response rain streaks in nighttime scenes, making a particularly effective choice for nighttime deraining tasks, whereas HSV and LAB show relatively weaker advantages in this regard.
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imags
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ICCANOO
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(I) (HSV); Nighttime
Figure 11: Histograms of channels in different color spaces for nighttime scenes.
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 defined as follows:
S = T(K(K( (, N(n)),
(13)
where N denotes the rain mask generated by random noise n. We utilize uniform random numbers and thresholding to control the noise level, with / and representing the length and angle of the motion blur kernel K E RPxP, respectively. Subsequently, we apply Gaussian blur to introduce a rotated diagonal kernel, controlling the rain width Finally, the transparency of the rain is controlled using the function T, generating the final rain mask S. The noise quantity n, rain length rain angle f, and rain thickness w obtained by sampling from (50, 200). [20, 50), [-30%, 30 and [3, 7].
Generation raindrop masks. Inspired by work [2], order to achieve higher quality and more realistic raindrop synthesis images, the open-source 3D graphics engine (Blender) to simulate
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Xuan fox
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teug!
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Jass-hox mage
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(k) (HSV); Daytime
(I) (HSV); Daytime
Figure 12: Histograms of channels in different color spaces for daytime scenes.
and generate real raindrop images. This 3D graphics engine can render raindrops using a physical motion model, allowing us set depth information and color values separately in the RGB channels [19], facilitated by 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 is represented by the function r[t. 0, Φ), where r is the distance from the surface of the droplet to its center, is the angle defined as the opposite direction to the point where the droplet falls, and 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 as follows:
rit, 0, rro(1 + Σ cos (m))
(14)
where ro represents the undeformed radius of the raindrop, and the factor cos(m)) depends on the droplet size ro. The function Pm (0) describes the time-dependent variation of the modal shape and amplitude relative to 0. As the raindrop falls, the effects of aerodynamic forces and surface tension acting on the droplet lead to rapid shape distortions over time.
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D Pseudo-code for Dataset Synthesis
We represent the dataset synthesis process using pseudocode, as shown in Algorithm 1.
Algorithm The pseudocode of our proposed framework for synthesizing datasets.
1: Input: Background B, type (Rain streak or Raindrop)
2: Output: Rain degradation image (Ons or ORD)
AALITATELLLLMNNNNNEEAAAA 4: BиSv RGBToHSV(B)
3: procedure
5: N +
6: I I 1-8
7: return /
8:
end procedure
9:
procedure GENERATEMASKSILZ type)
10:
if type = 'Rain streak' then
11:
RS1 <- GeenerateRannSttaakMakkk
12:
M &- RS2 t (RS))
13:
else if type 'Raindrop' then
14:
RD + GeenerateRanndoppMssk///
M RDD2 + (RD:)
16:
end if return MM
18:
end procedure
19:
procedure MAINPROCESS(B, M, type)
20:
if type 'Rain streak' then
21:
Ors + M]
22:
return Ors else lype = 'Raindrop' then
23:
24:
Butur p (B)
25:
OND < BaBuuur M]
return ORD
27:
end if
28:
end procedure
E Overview of the HQ-NightRain Dataset
Table 8 provides detailed overview of our proposed HQ-NightRain dataset, including the number of images each subset.
Table 8: Overview of our proposed HQ-NightRain dataset. The dataset includes rain streaks (RS), raindrops (RD), a mixture of rain streaks and raindrops (SD), real nighttime rain images (Real), and natural-scene nighttime rain images (Nature).
Number
Subset
RS
RD
SD
Real
Nature
Training set
5,000 (pairs)
2,500 (pairs)
2,500 (pairs)
Validation set
500 (pairs)
200 (pairs)
200 (pairs)
/
Testing set
100 (pairs)
100 (pairs)
100 (pairs)
512
20 (pairs)
F Dataset Visualization
In this section, we present additional sample images from the proposed -i-ghtttRain 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
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(a) RS
(b) RD
(c) SD
(d) Real
Figure 13: Example images from the HQ-NightRain dataset. The dataset includes rain streaks (RS), raindrops (RD), mixture of rain streaks and raindrops (SD), and real nighttime rain images (Real).
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comprehensive coverage, providing richer data resources for nighttime deraining tasks. Moreover, as our dataset includes corresponding JSON labels, enables object detection tasks to evaluate the impact of deraining results on downstream applications. We provide additional visual comparisons Figure 14.
with other datasets
Table 9: Comparison of existing daytime and nighttime deraining datasets. Our dataset includes wider variety of rain types: 'RS' represents rain streaks, 'RD' represents raindrops, 'SD' represents
mixture of raindrops and rain streaks, and 'Real' represents real-world data.
Rain Categories
Type
Datasets
Annotation
RS
RD
SD
Real
Rain200L/H [46]
None
DID/DDN-Data [14, 55]
None
Daytime
Raindrop [32]
None
Rain13K [21]
None
RainDS [33]
None
MPID [27]
Detection
Nighttime
GTAV-NightRain [53]
None
RoadScene-rain [35]
None
Raindrop Clarity [22]
None
HQ-NightRain (Ours)
Detection
(a) GTAV-NightRaiin
(b)
RoadScene-rain (c) HQ-NightRain- (d) Raindrop Clarity (e) HQ-NightRain-
[S3]
[35]
RS (Ours)
[22]
RD (Ours)
Figure 14: Further visual comparisons with other nighttime deraining datasets.
H
User Study
In this section, 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 total of 72 human evaluators. The first focuses on the illumination thresholds 71 and T2 used to calculate the illumination coefficient matrix in Equation 4 of the main manuscript. Multiple sets of nighttime rain images generated using various parameter combinations, and users select the images they perceive as most realistic. As shown in Figure 15, images generated with (Ti, T2) (0.2, 0.8) 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-basee four-layer encoder-decoder architecture [5]]. The expansion ratio of feature channels is set to and the number of modules in the first and second stages is set to (N1, N2, , N2 N2) and {NY, Nº NY, NY), respectively. Skip-
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0.28
0.26
0.24
0.22
Frequency
0.2
0.18
0.14
0.12
0.08
0.06
0.04
(0.1,0.8) (0.1.0.9) (0.2,0.7) (0.2.08) (0.1..9) (0.3.0.)
Figure 15: The box plot presents the results of the user study for perceived visual realism scores across different hyperparameters (T1, T2). Higher scores indicate better perceived realism.
Rain13k
GTAV-NightRain
RoadScene-rain
Raindrop Clarity
Rightigt (Ours)
10
20
30
40
50
Figure 16: The ridge plot presents the results of the user study, showing the number perceived realism selections across different datasets.
connections are incorporated bridge consecutive intermediate features, enabling stable training. The architectures of the modules used in the network are illustrated in Figure 18. Ablation experiments conducted 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 each stage, and the combination method at each stage.
Methods
(NT.N), NJ.
[N",N" NN".N..NT.M. NTT
Stagel
Stage2
PSNR1
SSIM
LPIPS}
(a) Ours
(1.2224)
(1,2224)
SERB+MSEN
MDTA+MSCM
40.4984
0.9881
0.0248
(1.2224)
(1,2224)
SERB-MSCM
MDTA+MSEN
39.0333
0.9846
0.0334
(c)
(1,224)
(1,22.4)
SERB+MSFN
MDTA+MSCM
38.8534
0.9842
0.0336
(d)
(2,4.66)
(2.4.66)
SERE+MSHN
MITTA+MSCM
39,7594
0.9861
0.0301
(0)
[4.6.6.8]
(4.6.6.8
SERB+MSEN
MDTA+MSCM
40.1056
0.9873
0.0275
J
Loss Function
In this document, we provide a supplementary introduction to the loss function used. For the degradation removal stage, utilize the Mean Squared Error (MSE) loss function, defined as follows:
Lmar Σ (Y - -)²
(15)
where Y₁, represents the Y channel the ground-truth image, and Y denotes the predicted result from the degradation removal stage. Additionally, we incorporate Charbonnier [52], Structural Similarity
28

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H x x
2c
4
BC
4 x
2
#xWxC
Blocks
x N1
x N2
x N3
N4
x N
x 222
x N
Figure 17: Network architectures of the degradation removal stage and the color refinement stage.
(a) Squeeze-Excitation Residual Block (SERB)
(b) Mixed-scale Feed-forward Network (MSFN)
Concul
ReLU
ReLU
ReLU
MaxPool
Cony
Cony
7x7
1x1
(c) Multi-Deonv Head
Transposed Attention
(MDTA)
Multi-Scale Convolutional Module (MSCM) Figure 18: Detailed structure of the modules used in the network
(SSIM) loss and edge [10] loss constrain network training. The Charbonnier loss is defined as follows:
|IOGE Loa +2,
Lcher =
(16)
where ORGB denotes the reconstructed image output by the network, Itt represents the ground truth image and 10-3 is an offset value. The structural similarity loss is defined follows:
Lasim = SSIM(O - -gt))
(17)
The edge loss is defined follows:
EW H
Fig
(1or ORGBLS)
Ledge
(18)
WH
where W and H represent the width and height of the image, respectively. The proposed loss function Liorat for network training is defined as follows:
Liotal Linee + Lasim Lchar Ledges
(19)
where a is empirically set to 0.5.
K Model Complexity
Table 11 presents the complexity comparison. With reduced number of modules, our method avoids significant increases in model complexity. Compared other state-of-the-ar methods, our model demonstrates certain ad advantages.
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Table 11: Comparisons of model complexity against state-of-thh-art methods. The size of the test image is 256 x 256 pixels.
Methods
RCDNet [37]
MPRNet [52]
Restormer [51]
#FLOPs (G)
194.502
548.652
140.990
#Params (M)
2.958
3.637
26.097
Methods
NeRD-Rain [6]
DRSformer [5]
CST-Net (Ours)
#FLOPs (G)
147.978
220.378
144.819
#Params (M)
22.856
33.627
16.207
L Hyperparameter Validation
As shown in Table 12. validate the illumination threshold hyperparameters Ti and 72 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 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 RealRain k-- [28]. The results demonstrate that our settings enhance the realism of the dataset and achieve optimal generalization performance.
Table Illumination threshold hyperparameter verification (PSNR SSIM).
(T1.72)
Random
(0.1, 0.7)
(0.3, 0.9)
(0.2, 0.8) (Ours)
HQ-NightRain-RS (Ours)
40.78 / 0.9909
01.917
42.13/ 0.99/1
42.89 / 0.9924
RcalRainIk-L
26.08 / 0.8758
/
26.73 0.8840
27.31 / 0.8891
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, apply this technique to film and game production 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.
(a) Game screenshot
(b) Rendering of rain scene
Figure 19: An example of our rain synthesis technique applied to Resident Evil 2 game scenes.
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.
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Table 13: Performance comparison joint image deraining. and object detection on the BDD350-
Night dataset [21].
Methods
Rain
PReNet
RCDNet
IDT
Restormer
SPNet
DRSformer
RL.P
NeRD-Rain
CST-Net (Ours)
Deraining:
Dalaset: BDD355-NighH; Image Size: 1280 x 720
PSNRT
10.7687
11,0005
11,783
12.1124
12.2404
12,0769
12.2477
11,7422
12,1508
12,3884
SSIM?
0,1773
0,1901
0,1967
021011
0.2141
0.2229
0,2224
0.1931
0.2101
0.2244
Object Detection: Algorihm:: YOLOvE:
Dalaset:
BDD350-Night,
Threshold: 0.6
Precision(
16.00
14.72
18.71
16.20
20.24
19.43
18.37
13.69
20.19
20.49
Recall(S)
4.66
4.66
6.21
5.63
6.60
6.60
6.99
4.47
8.16
8.16
(nU(S))
20.26
20.34
22.27
21.36
23.08
22.77
21.56
18.42
22.27
23.29
(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) Restormer [51]
(e) SFNet [12]
DRSformer [S]
NeRD-Rain [6]
(h) CST-Net (Ours)
Figure 20: Comparison of image deraining and object detection on the BDD350-Night dataset [21].
31

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(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) Restormer [51]
(c) SFNet [12]
(f) DRSformer [5]
(g) NeRD- Rain [6]
(b) CST-Net (Ours)
Figure 21: Comparison of image deraining and object detection on the BDD350-Night dataset [21].
32

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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-NightRan [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.
(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) SPDNet [48]
(c) IDT [42]
Restormer [51
SFNNet [12]
(h) DRSformer
[54]
ii) MSGNN [36]
(k) NeRD-Rail [6]
(1) CST-Net (Ours)
Figure 22: Visual comparison results on the GTAV-NightRain dataset [53]. The results shown in
(b)-(k) still contain significant rain streaks. In contrast, our models generate much clearer images.
33

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(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) SPDNet [48]
(e) IDT [42]
(I) Restormer [51]
SFNet [12]
(h) DRSformer
RLP [54]
i MSGNN [36]
(k) NeRD-Rail Rain [6]
(I) CST-Net (Ours)
Figure 23: Visual comparison results on the GTAV-NightRain dataset [53]. The results shown in
(b)-(k) still contain significant rain streaks. In contrast, our models generate much clearer images.
34

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(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) MPRNet [52]
(e) IDT [42]
(I) Restormer [51]
SFNet [12]
(h) DRSformer [5]
(i) RLP [54]
(i) MSGNN [36]
(k) NeRD-Rain [6]
(1) CST-Net (Ours)
Figure 24: Visual comparison results on the RS subset of the RainDS-real dataset [33] reveal that the evaluated methods fail to produce clear images, with some structural details not well restored. In contrast, method generates derained images with finer structural details and improved clarity.
35

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(a) Rainy Input
bb) PReNet [34]
(c) RCDNet [37]
(d) MPRNet [52]
(e) IDT [42]
(I) Restormer [51]
gg) SFNet [12]
(h) DRSformer [5]
RIP [54]
(i) MSGNN [36]
(k) NeRD-Rain [6]
()) CST-Net (Ours)
Figure 25: Visual comparison results on the RD subset of the RainDS-real dataset [33] reveal that the evaluated methods fail to produce clear images, with some structural details not well restored. In contrast, our method generates derained images with finer structural details and improved clarity.
36

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(a) Rainy Input
(b) PReNet [34]
(c) RCDNet [37]
(d) MPRNet [52]
(e) IDT [42]
(I) Restormer [51]
(g) SFNet [12]
(h) DRSformer [5]
RLP [54]
() MSGNN [36]
(k) NeRD-Rain [6]
()) CST-Net (Ours)
Figure 26: Visual comparison results the RSD subset of the RainDS-real dataset [33] reveal that the evaluated methods fail to produce clear images, with some structural details not well restored. In contrast, method generates derained images with finer structural details and improved clarity.
37


Reviews:
[{"review_id": "WRVL6ljxb3", "reviewer": "Reviewer_jK5Y", "summary": "This 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.", "questions": "### 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.", "limitations": "***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?", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### 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?", "was_revised": true, "final_justification": "Taking into account the other reviewers’ comments and the authors’ detailed rebuttal, I believe the paper makes a valuable contribution and recommend acceptance.", "rebuttal": "We thank Reviewer jK5Y for his\\her efforts. We are encouraged by the reviewer’s positive comments on the dataset quality, effective method, paper presentation, and solid experiments. We answer the questions below and will incorporate all feedback in the revised version.\n\n \n\n **1. Extend to video deraining and snow removal**\n\nFor snow removal task, we have provided the experimental results on the multi-weather dataset Multi-Weather6k (including rain, snow, and haze) in **Table 6 of the main paper** and elaborate on them in **Section 6.5**, demonstrating that our method is also effective for image desnowing. \n\n \n\nFor the video deraining task, we use the RainVID&SS [1] dataset, where the Cam-Vid subset consists of training frames extracted from 3 long clips and testing frames extracted from 2 long clips. We adopt this subset and train all models using patches of size 128×128 pixels. We compare our method with two video deraining approaches, S2VD [2] and MPEVNet [1], and the quantitative results are shown in the table below. The results demonstrate that our CST-Net still exhibits strong restoration potential in the video deraining task. We will add these results in the revised paper.\n\n \n\n| Methods |  S2VD   | MPEVNet | CST-Net (Ours) |\n| :-----: | :-----: | :-----: | :------------: |\n| PSNR ⬆  | 27.6059 | 31.3213 |    31.8259     |\n| SSIM ⬆  | 0.8755  | 0.9252  |     0.9366     |\n\n \n\nRef:\n\n[1] Sun, Shangquan, et al. “Event-aware video deraining via multi-patch progressive learning.” IEEE TIP 2023.\n\n[2] Yue, Zongsheng, et al. “Semi-supervised video deraining with dynamical rain generator.” CVPR 2021.\n\n \n\n **2. Issues of Fig. 1**\n\nThe histogram statistics in Fig. 1 are based on multiple paired images from the public RealRain1k dataset. Due to rebuttal format limitations, we are unable to provide corresponding image pair examples here. Note that we normalize the background luminance before analyzing the rain effects in the Y channel. By doing so, our approach ensures that the analysis focuses on the nighttime rain features themselves, rather than being dominated by lighting conditions in the scene. We will clarify this in the revised paper.\n\n \n\n **3. Missing references**\n\nThanks for the suggestion. We will add the suggested references in the revised paper."}, {"review_id": "yuMvvuGyiu", "reviewer": "Reviewer_aW1g", "summary": "This 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.", "questions": "1. 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?", "limitations": "1. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength：\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.", "was_revised": true, "final_justification": "Based on the author's response and the comments of other reviewers, I gave the final score.", "rebuttal": "We thank Reviewer aW1g for his\\her efforts. We are encouraged by the reviewer’s positive comments on our dataset contribution, model framework innovation, and excellent performance. We answer the questions below and will incorporate all feedback in the revised version.\n\n**1. Reliance on fixed hyperparameters**\n\nIn our dataset construction, our thresholding strategy is based on observations from a large number of real nighttime rainy images, where rain streaks are nearly imperceptible in extremely dark or overly bright regions. To justify our choice of hyperparameters, we have provided a user study and validation results in **Sections H and L of the supplementary material**, respectively. As shown in **Table 11 of the supplementary material**, we tested random thresholds as well as various threshold combinations. The results demonstrate that our chosen thresholds yield the best performance. To further assess the robustness and generalization capability of different threshold settings, we trained models on datasets generated with different threshold values and evaluated them on the real-world RealRain1k-L dataset. Our fixed threshold setting achieved the best performance, supporting its effectiveness and rationality.\n\nFurthermore, as illustrated in **Figure 15 of the supplementary material**, we have conducted an online user study to evaluate the perceptual quality of images generated with different threshold settings. The results show that our setting received the highest preference, indicating that our dataset is more consistent with human perception and better aligned with real-world visual appearance.\n\n**2. Simplifications in the synthesis pipeline**\n\nThe core of our data synthesis pipeline lies in the incorporation of illumination information from background images. The resulting non-uniform rain masks align with nighttime imaging characteristics, significantly enhancing the realism of the dataset compared to existing synthesis methods. As shown in **Figure 16 of the supplementary material**, user study results indicate a competitive preference for our synthesized images, suggesting better alignment with human perception. In future work, we will continue to explore advanced synthesis strategies to better reflect real-world optical complexities and further improve the realism of our dataset.\n\n**3. Limited scope to single degradation**\n\nWe would like to claim that our method not only solves the deraining task but also handles mixed adverse conditions. As shown in **Table 6 of the main paper**, we have provided experimental results on the Multi-Weather6k dataset, which includes rain, snow, and haze scenarios. The results indicate that our model has promising potential for restoring images degraded by mixed adverse weather conditions.\n\nAs for the robustness in more complex multi-degradation scenarios, we have evaluated on the RealRain1k dataset, which includes both daytime and nighttime rainy scenes, and the RainDS-real dataset, which contains rain streaks, raindrops, and their combinations, as shown in **Table 2 of the main paper.** The results demonstrate that our method achieves robust performance across these diverse real-world conditions. Qualitative results are presented in **Figures 24, 25, and 26 of the supplementary material**.\n\nThe effectiveness of our model in addressing the above issues stems from its core design: a learnable mapping from the RGB color space to a luminance-dominated domain, which enables more effective degradation modeling. Since degradations such as snowfall also significantly affect image brightness and contrast, their visual artifacts become more prominent in the Y channel. As a result, CST-Net is not limited to nighttime rain degradation but can also handle other degradation types that impact luminance structures. In addition, our proposed Implicit Illumination Guidance (IIG) helps disentangle illumination and structure at the feature level, allowing the model to capture degradation-specific patterns under diverse conditions and guide the restoration process accordingly. Finally, our training paradigm does not rely on degradation-specific structural priors, thus demonstrating strong transferability across various weather-related degradation scenarios.\n\nFor rain combined with fog or haze, we have discussed the limitations in **Section 6 of the main paper**. In future work, we will further consider expanding the extra dataset under low-light conditions to include veiling effects and explore the incorporation of physical models to address this problem.\n\n**4. Issues of the data synthesis pipeline**\n\nThe traditional linear addition method adopts Equation (1) in the main paper, $R_s=B+S$, where rain streaks $S$ are simply added to the clean background $B$. This simpler addition ignores the interaction between rain streaks and the background, often resulting in a noticeable “sticker-like” effect. This is particularly evident around object boundaries, where rain streaks may unnaturally overlap with edges. As shown in the example image in **Figure 3(a) of the main paper.**\n\nIn our paper, we adopt Equation (2) in the main paper, $R_s=f[B,\\sigma(S)]$, where a 3×3 convolution operation $f[\\cdot]$ is applied to model the local interactions between rain streaks and the background. Rather than treating rain as pixel-wise additive noise, this approach introduces contextual perturbations to surrounding pixels, more accurately reflecting the physical process of rain formation in real-world imagery.\n\nWe further compare the impact of different fusion strategies on the final dataset's quality, and the results are shown in the table below. The quantitative results show that our merge strategy achieves the best performance in the NIQE [1] and NRQM [2] non-reference metrics, significantly improving the quality of the dataset. We will add these results in the revised paper.\n\n| Methods | Linear addition | Convolutional  merge $f[\\cdot]$ | NIQE⬇ | NRQM⬆ |\n| :-----: | :-------------: | :-----------------------------: | :---: | :---: |\n|   M1    |        √        |                                 | 5.93  | 5.45  |\n|  Ours   |                 |                √                | 5.28  | 6.37  |\n\n\nRef:\n[1] Anish Mittal, et al. “Making a completely blind image quality analyzer.” SPL 2012.\n[2] Ma, Chao, et al. “Learning a no-reference quality metric for single-image super-resolution.” CVIU 2017.\n\n**5. Effect of data synthesis pipeline components on model performance**\n\nAccording to the reviewer’s suggestion, we further add experiments to evaluate the effect of each component in the data synthesis pipeline on the final model’s performance. Specifically, we analyze our model performance on datasets generated using different synthesis pipeline configurations, and the results are shown in the table below. As observed, our data synthesis pipeline incorporates convolutional merge $f[\\cdot]$, illumination merge $\\sigma[\\cdot]$, and defocus blur $\\rho[\\cdot]$, which better reflect real-world degradation conditions. We will add these results in the revised paper.\n\n| Methods | Linear  addition | Convolutional  merge $f[\\cdot]$ | Illumination  merge $\\sigma[\\cdot]$ | Defocus  blur $\\rho[\\cdot]$ | PSNR ⬆  | SSIM  ⬆ |\n| :-----: | :--------------: | :-----------------------------: | :---------------------------------: | :-------------------------: | :-----: | :-----: |\n|   D1    |        √         |                                 |                                     |                             | 25.3894 | 0.8705  |\n|   D2    |                  |                √                |                                     |                             | 28.5863 | 0.9456  |\n|   D3    |                  |                √                |                  √                  |                             | 36.5540 | 0.9754  |\n|  Ours   |                  |                √                |                  √                  |              √              | 31.9161 | 0.9493  |\n\n\n**6. How the model performs in colored rain streak removal?**\n\nAs shown in **Figure 22 of the supplementary material**, our method effectively removes colored rain streaks influenced by yellow light sources, demonstrating strong robustness in handling colored rain streaks. If the Cb/Cr channel features are directly passed to the color refinement stage, it would not cause color artifacts. The underlying reason is that we designed the Implicit Illumination Guidance (IIG) module, which guides the model’s attention toward complex rain streaks near light sources (including colored rain streaks) and extracts implicit degradation information to guide the second-stage color refinement process."}, {"review_id": "UKVj2i1hjO", "reviewer": "Reviewer_bmcM", "summary": "This 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.", "questions": "As 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths：\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.", "was_revised": true, "final_justification": "Based on the author's response and the comments of other reviewers, I gave the final score.", "rebuttal": "We thank Reviewer bmcM for his\\her efforts. We are encouraged by the reviewer’s positive comments on the dataset contribution and very helpful research. We answer the questions below and will incorporate all feedback in the revised version.\n\n**1. Novelty on color space transformation**\n\nOur novelty in color space transformation lies in introducing a **learnable nonlinear** coefficient matrix to replace the traditional **fixed linear** transformation used in previous methods (as in the reviewer-mentioned paper [1], which describes “using a linear transformation defined by standard conversion matrices”). Specifically, as shown in **Equations (8) and (9) of the main paper**, each transformation weight is replaced with a learnable variable and further processed through a multilayer perceptron (MLP) for nonlinear transformation. This approach not only retains the ability to handle luminance during the color space conversion but also enables adaptive parameter adjustment based on specific datasets and application scenarios. It adapts to various lighting conditions, scene types, and content characteristics, making the extracted features more robust to the complexity and randomness of nighttime rain effects. We will add a discussion with [1] in the revised paper.\n\nRef:\n\n[1] Fang, Wenxuan, et al. “Guided real image dehazing using YCbCr color space.” AAAI 2025.\n\n**2. Innovations in network design and training process**\n\nWe would like to claim that the main innovation of our method lies in formulating **a new end-to-end trainable two-stage framework** for better nighttime image deraining. We decompose the complex nighttime deraining task into a degradation removal stage and a color refinement stage for joint learning. Although we use some common modules and loss functions as the base backbone, our focus lies in developing **two new components** to better help the feature learning of these two stages. Firstly, we develop a **learnable Color Space Converter (CSC)** in the degradation removal stage to better facilitate nighttime rain removal in the Y channel. Second, instead of employing explicit illumination models for brightness estimation as in existing methods, we introduce a new **Implicit Illumination Guidance (IIG)** module in the color refinement stage, which leverages implicit neural representation technology to encode complex illumination information. We will clarify this in the revised paper.\n\n**3. Issues of t-SNE distribution**\n\nThe t-SNE distribution figure shows that our dataset more closely aligns with the distribution of real-world nighttime rainy images. To prove this and avoid the influence of different background content on the results, as stated in the caption of Figure 3 in the main paper, we select samples with similar nighttime street backgrounds and use ResNet50 to extract features. After applying t-SNE dimensionality reduction, images from the same dataset exhibit clustering due to the similarity in rain streak features. By doing so, the distance between different data distributions more accurately represents the size of their rain distribution differences. Note that, due to the domain gap between the synthetic dataset and real-world data, there is low overlap between the different distribution clusters.\n\nIn this paper, our goal is to enhance the quality and realism of the synthetic nighttime rain images to reduce the domain gap with real-world data. As shown in the t-SNE distribution visualization, compared to other existing datasets, our proposed dataset successfully narrows the domain gap with real images, which further indicates that our dataset synthesis pipeline generates nighttime rain patterns that are closer to real-world conditions."}, {"review_id": "OZMq170jhf", "reviewer": "Reviewer_ohMd", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes, the authors have discussed the related limitations.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\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.", "was_revised": true, "final_justification": "After further discussion with the author, I still maintain my acceptance decision.", "rebuttal": "We thank Reviewer ohMd for his\\her efforts. We are encouraged by the reviewer’s positive comments on the paper presentation, dataset contribution, and superior performance. We answer the questions below and will incorporate all feedback in the revised version.\n\n**1. Details on architecture**\n\nWe have provided the implementation details of CST-Net in **Section I of the supplementary materials**. \n\n**2. Effectiveness in daytime scenes**\n\nWe would like to claim that we have evaluated our method on the RealRain1k and RainDS-real benchmark datasets, both of which contain rainy images captured in daytime scenes. As shown in the quantitative results in **Table 2 of the main paper**, our method still achieves good performance on both datasets. We have provided visual examples in **Figures 24, 25, and 26 in Section O of the supplementary material**, demonstrating that our model also achieves robust deraining performance in daytime rainy scenes.\n\n**3. Comparison with Mamba-based methods**\n\nAccording to the reviewer’s suggestion, we further compare our method with recent Mamba-based method, FreqMamba [1]. As shown in the table below, our method outperforms FreqMamba across all three subsets, including rain streaks (RS), raindrops (RD), and the mixture of rain streaks and raindrops (SD). We will add these results in the revised paper.\n\n| Datasets  | HQ-NightRain-RS           | HQ-NightRain-RD           | HQ-NightRain-SD           |\n| :-------- | :------------------------ | :------------------------ | :------------------------ |\n| Methods   | PSNR / SSIM / LPIPS       | PSNR / SSIM / LPIPS       | PSNR / SSIM / LPIPS       |\n| FreqMamba | 42.1469 / 0.9909 / 0.0152 | 33.0119 / 0.9455 / 0.1520 | 37.5566 / 0.9791 / 0.0498 |\n| Ours      | 42.8850 / 0.9924 / 0.0100 | 33.9395 / 0.9523 / 0.1239 | 40.4984 / 0.9881 / 0.0248 |\n\nRef:\n\n[1] Zhen, Zou, et al. “FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining.” ACM MM 2024.\n\n\n**4. Generation of rain streak and raindrop masks**\n\nWe have provided detailed descriptions of the generation processes for rain streak masks and raindrop masks in **Section C of the supplementary material**.\n\n\n**5. Issues of the defocus blur operation**\n\nAs stated in **L135–138 of the main paper**, we introduce defocus blur based on real-world observations to simulate more realistic nighttime rainy images. The defocus blur is applied to the rain-free nighttime background, without considering the overall nighttime illumination distribution of the image. Illumination distribution is only considered during the synthesis of the rain mask to generate more realistic, non-uniform masks."}]

Author rebuttal:

Meta-review: This paper introduces a new benchmark dataset (HQ-NightRain) and a learnable color space transformation framework (CST-Net) for nighttime image deraining. Reviewers agree that the paper is technically solid, clearly presented, and supported by comprehensive experiments. The dataset is a timely and valuable contribution, and the proposed method demonstrates strong effectiveness and robustness across multiple benchmarks. While some reviewers raised concerns about novelty and simplifications in dataset synthesis, the authors provided thorough rebuttals and additional results that addressed these issues satisfactorily. Overall, the strengths outweigh the weaknesses, and all reviewers support acceptance.
Decision: Accept (poster)