Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling
Authors: Long Peng
Anran Wu
Wenbo Li
PeizheXia
Xinjie Zhang
Xueyuan Dai
Xin Di
Haoze Sun
Renjing Pei
Yang Wang
Yang Cao
Zheng-Jun Zha
Venue: ICLR 2026 Poster (2026)
Keywords: Continuous Super-Resolution; 2DGS; Fast Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained to fixed-scale factors (\textit{e.g.}, $\times$ 2). Recent advances leveraging implicit neural representation (INR) have achieved great progress by modeling coordinate-to-pixel mappings. However, the efficiency of these methods may suffer from repeated upsampling and decoding, while their reconstruction fidelity and quality are constrained by the intrinsic representational limitations of coordinate-based functions. To address these challenges, we propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting. This approach eliminates the need for time-consuming upsampling and decoding, enabling extremely fast ASSR. Once the Gaussian field is built in a single pass, ContinuousSR can perform arbitrary-scale rendering in just 1ms per scale. Our method introduces several key innovations. Through statistical analysis, we uncover the Deep Gaussian Prior (DGP) and propose DGP-Driven Covariance Weighting, which dynamically optimizes covariance via adaptive weighting. Additionally, we present Adaptive Position Drifting, which refines the positional distribution of the Gaussian space based on image content, further enhancing reconstruction quality. Extensive experiments on seven benchmarks demonstrate that our ContinuousSR delivers significant improvements in SR quality across all scales, with an impressive 19.5× speedup when continuously upsampling an image across forty scales.

Published as a conference paper at ICLR 2026
PIXEL TO GAUSSIAN: ULTRA-FAST CONTINUOUS SUPER-RESOLUTION WITH 2D GAUSSIAN MODELING
Long Pengl.21 Anran Wult Wenbo Li Peizhe Xia
Xueyuan Dai
Xinjie Zhang
Xin Di Haoze Sun Renjing Pei Yang Wang 1.3
Yang Cao
Zheng-Jun Zha
USTC
Huaaei Noah's Ark Lab
Chang" an University
*HKUST 6THU "CUHK
attps://gittnn com pogg Cont inuousRR
Scales
Number of Scale N
HR
OMI
ISP
Docoder
Pux
HR
LR
ContinaousRR
ms
86XN ms
40 xN ms
Continuous Real World
a. Existing Methods Total Time: 136-86XN+40 XN ms
b. Motivation
Scales 4pnro 8,
Solk Sunde Thom
6375ars
Orcheenn
Gaudt
Rendering
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SR
201
229
270mrc
Continuoss
1xNN ms
Our Method
Total Time: 229+1xN ms
d. Speed and PSNR Comparison
# dIl
+-0.99
Figure I: (a) Leveraging implicit modeling, existing ASSR methods rely on multiple upsampling and decoding steps to reconstruct HR images at different scales, which leads to low efficiency and performance. (b-d) Our method explicitly reconstructs 2D continuous HR signals from LR images in single pass. Then, fast rendering replaces the time-consuming upsampling and decoding process reconstruct HR images at different scales, achieving significant improvements over the state-of- the-art method GSASR in both performance (0.15 18 dB in Mangal (99) and efficiency (19.5x speedup).
ABSTRACT
Arbitrary-scale super-resolutins (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained fixed-scale factors (e.g., 2). Recent advances leveraging implicit neural rep- resentation (INR) have achieved great progress by modeling coordinate-to-pixt mappings. However, the efficiency of these methods may suffer from repeated upsampling and decoding, while their reconstruction fidelity and quality are con- strained by the intrinsic representational limitations of foordinate-based func- tions. To address these challenges, we propose novel ContinuousSR framework with Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting. This approach eliminates the need for time-consuming upsampling and decoding, enabling extremely fast per-
ASSR. Once the Gaussian field is built in single pass, ContinuousSR form arbitrary-scale rendering in just Ims per scale. Our method introduces sev- eral key innovations. Through statistical analysis, we uncover the Deep Gaussian Prior (DGP) and propose DGP-Driven Covariance Weighting, which dynamically optimizes covariance via adaptive weighting. Additionally, we present Adaptive Position Drifting, which refines the positional distribution of the Gaussian space based on image content, further enhancing reconstruction quality. Extensive ex- periments on seven benchmarks demonstrate that our ContinuousSR delivers sig- nificant improvements in SR quality across all scales, with an impressive 19.5x speedup when continuously upsampling an image across forty scales.
Cooresponding Authors.
These authors contributed equally this work.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Cameras and smartphones discretize continuous real-world scenes into discrete 2D digital im- ages Castleman (1979); Jain (1989); Xia et al. (2024), illustrated in Figure 1(b). However, limita- tions in sensor resolution, among other factors, often lead to low-resolution (LR) images that fail to meet user requirements. Image super-resolution (SR) has been proposed to enhance image resolution and finer details Wang et al. (2020); Liu et al. (2022). Unlike traditional fixed-scale super-resolution Peng et al. (2024a); Liang et al. (2021); Chen et al. (2023c); Lim al. (2017a); Zhang et al. (2018), which uses multiple models to learn mappings for fixed scales (e-g-, x2, x3, x4), arbitrary-scale super-resolution (ASSR) employs a single model to handle super-resolution with arbitrary scales, which has attracted significant attention Hu et al. (2019); Wei & Zhang (2023); Chen et al. (2023a); Lee & Jin (2022a); Liu et al. (2024); Wan et al. (2024); Li et al. (2024c); Liu & Tang (2025).
Among these approaches, implicit neural representation (INR) has emerged as a leading technique, delivering visually compelling results Chen et al. (2021): Lee & Jin (2022b); Xu et al. (2021); Cao et al. (2023); Xia et al. (2024); Jiang et al. (2025). INR aims to learn a continuous mapping from pixel coordinates to pixel values, enabling arbitrary-scale super-resolution through multiple upsam- pling and decoding steps, as illustrated in Figure [(a). For example, LIIF Chen et al. (2021) is the first to introduce INR into ASSR, employing multi-layer perceptrons to learn this mapping. Later, CiaoSR Cao et al. (2023) and CLIT Chen et al. (2023a) leverage Transformers to enhance the mod- eling of long-range dependencies in feature upsampling and decoding. However, the reconstruction fidelity and quality of these methods are inherently constrained by the representational limitations of coordinate-based implicit functions, making it challenging to effectively model continuous high- resolution signals, ultimately leading suboptimal performance. Recent work has explored Gaus- sian representations for ASSR as an alternative to coordinate- (2024) models Gaussians in the feature space but, like -NR-based approaches, still requires a dedi- cated decoding pass for every target scale, resulting in high computational cost. GSASR Chen et al. (2025) predicts scale-conditionel 2D Gaussians in RGB space, but regenerates a separate Gaussian set for each scale, which limits both cross-scale consistency and efficiency.
mappings. GaussianSR Hu et al.
Given that LR images are discretized from continuous 2D signals, we pose the fundamental question: "Can we directly reconstruct continuous HR signals from LR images and flexibly choose the desired scale?" 2d As illustrated in Figure 1(b), this approach not only enhances signal continuity through continuous cadéeling-ecadinn to improved reconstruction quality- but also significantly boosts efficiency by eliminating the need for time-consuming upsampling and decoding. This idea enables fast and flexible ASSR, making real-world applications more practical.
In this paper, we present the ContinuousSR framework built upon the -ee-ss-aaa paradigm, which reconstructs 2D continuous HR signals through Gaussian modeling. By recovering contin- uous high-resolution Gaussian field, our method enables fast sampling directly from the continuous representation, effectively replacing conventional time-consuming upsampling and decoding proce- dures. This design leads to consistently strong reconstruction quality while requiring only ms for ASSR inference, delivering substantial improvement in efficiency.
Directly applying Gaussian modeling to simulate real-world images is highly challenging due to the intricate interweaving of pixel distributions and parameters. To address this, we first identify the Deep Gaussian Prior (DGP), revealing that the distribution of Gaussian field parameters follows a Gaussian pattern with regularities in their range, as shown in Figure 2(a-b). Leveraging this in- sight, we sample pre-defined Gaussian kernels from the DGP and introduce a novel DGP-Driven Covariance Weighting module, which efficiently optimizes covariance via adaptive weighting. This helps guide the model toward the global optimum. Furthermore, we propose Adaptive Position Drifting module, which dynamically adjusts the spatial positions of Gaussian kernels based on im- age content, enhancing structural accuracy. With these innovations, our method not only surpasses state-of-thh-an approaches by up to 0.18 dB in reconstruction performance but also achieves 19.5x speedup when continuously upsampling across forty scales. Our main contributions are as follows:
A novel ContinuousSR is proposed to reconstruct continuous HR signals from LR images by 2D Gaussian modeling, thereby enabling fast and high-quality super-resolution with arbitrary scale.
2

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Published as a conference paper at ICLR 2026
End End Model without Prior
Can. space bigger (2) Gan. space sensilive
Why (a) fail?
EZE Model
Nosse
2631
Analysis
LR
Space
HR
Nose
13333 cB
ContinaousRR with Deep
Comparison with Method (t)
Deep Gaussian Prior (DGP)
dlo
pa.az
Loss
Triliing Lom tom Spoct
PSNR
COPERFA2OORN
Natural Images
Covariance Prior
Continuous HR
FR
Epoch
Color Mapping
Position Drifting
ContinnousRR
E26
Ours
FRR
Figure 2: (a) Directly learning the end-to-end model from LR to the Gaussian field is challenging due to the vastness and sensitivity of the Gaussian space. (b-c) Through statistical analysis of 40,000 natural images, we uncover the Deep Gaussian Prior and propose Position Drifting, Covariance Prior, and Color Mapping to propose the ContinuousSR, enhancing the quality of the Gaussian field.
The Deep Gaussian Prior (DGP) is discovered, based on which DGP-Driven Covariance Weight- ing is proposed to facilitate the optimization of covariance. Furthermore, Adaptive Position Drift- ing is introduced to dynamically learn spatial positions in Gaussian space.
Extensive experiments demonstrate that our method achieves state-of-the-art performance seven benchmarks and ultra-fast speed.
RELATED WORK
2.1 ARBITRARY-SCALE SUPER-RESOLUTION
Although traditional fixed-scale super-resolution (FSSR) methods, which use separate models to learn different super-resolution scales, have achieved significant progress Dong al. (2014); Ledig et (2017); Zhang et al. 22018); Kim et al. (2016); Cavigelli et al. (2017) 1); Zhang et al. (2021); Wang al. (2018); Di al. (2024); Peng et (2024b), they struggle to meet the demand for arbitrary- scale super-resolution in real-world scenarios. Additionally, maintaining multiple models incurs high computational costs, making them less practical. To address these limitations, Arbitrary-Seale (ASSR) has been proposed to achieve it with single model, gaining increasing attention in recent years Cao et al. (2023); ; Hu et al. (2019); Fu et al. (2024); He & Jin (2024b); Zhu et al. (2025); Zhao et al. (2024); Tsai et al. (2024); Fu et al. (2024); Zhang et al. (2024a): Shang al. (2024); Jiang et al. (2024); Duan et al. (2024); He Jin (2024a); Jiang et al. (2025); Liu & Tang (2025). For example, MetaSR Hu et al. (2019) to achieve arbitrary-scale super-resolution. demonstrating promising results. Inspired by the success of implicit neural representation (INR) in 3D reconstruction, LIIF Chen al. (2021) was the first to adapt INR to super-resolution by using a multilayer perceptron to learn the mapping from image coordinates and features RGB values. To capture more (2022b) encodes textures in the Fourier space, while SRNO Wei & Zhang (2023) leverages neural operators model global relationships. CLIT Chen et (2023a) introduces a cross-scale interac- tion mechanism to enhance feature learning by integrating information across different resolutions. CiaoSR Cao et al. (2023) further improves long-range modeling capability by introducing transform- ers to INR, achieving state-of-the-art performance. LMF He & Jin (2024b) enhances local texture details by combining multi-frequencyy information in a computationally efficient manner, signifi- cantly reducing computational costs while maintaining the reconstruction of fine- fine-grained features. However, these implicit modeling methods struggle to explicitly reconstruct continuous HR signals and require time-consuming upsampling and decoding, leading to low performance and efficiency.
the first introduce the meta-upscale module
details, LTE Lee Jin
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2.2 GAUSSIAN SPLATTING
Gaussian Splatting (GS) is introduced into 3D as faster, more efficient alternative to NeRF, using anisotropic 3D Gaussians for real-time rendering and direct scene manipulation Kerbl et al. (2023). Building on 3DGS, 2D Gaussian Splatting improves the geometric accuracy of radiance fields by combining 2D Gaussians with precise scene projections Huang et al. (2024). Recently, 2DGS finds applications in image processing Dong et al. (2025). For instance, Gaussianlmage Zhang et al. (2024b) proposes leveraging GS for image compression and reconstruction through long-time op- timization of GS parameters, while GaussianSR Hu et al. (2024) applies Gaussian Splatting in the feature space to enhance visual quality and speed, and GSASR Chen et al. (2025) predicts scale- conditioned Gaussians for ASSR. However, these methods still struggle to reconstruct continuous HR signals and suffer from long optimization times or repeated upsampling and decoding.
3 MOTIVATION
To capture the real world, advanced imaging sensors (e.g., CMOS) are used to project the 3D contin- uous world into 2D and then discretized 21) continuous signals into 2D discrete signals Castleman (1979); Jain (1989); Xia et al. (2024), as formulated:
[m, n) fe(mArr
where fo(2, y) represents the continuous intensity function in the spatial domain (z. v). The Ar and Ay denote the sampling step along the spatial dimensions, while m, n E Z are the corresponding discrete pixel grids. [m, rı] represent the discrete images. After that, the Image Signal Processor is used to quantize, process, and encode into a digital low-resolution image ILR-
Although many methods leveraging implicit modeling have been proposed Chen et al. (2021); Lee & Jin (2022a) to achieve ASSR by constructing coordinatc-to-pixc. mappings, two major challenges remain. On the one hand, the aim of ASSR is to reconstruct fa(a,, y). but implicit modeling makes it difficult to explicitly model high-quality continuous functions, resulting in limited performance. On the other hand, the pipeline of INR-based ASSR methods suffers from low efficiency, follows:
FLR B(IR)), Fin = D(FLR.)). 8), 1 D(Fin).
(2)
where E, U and D represent the Encoder, Upsampling, Decoder, respectively, and FHR denotes the high-resolution feature map at scale 8. It can be observed that for different scales 8, this method re- quires multiple time-consuming upsampling U and decoding TD processes to reconstruct HR images Fin: as shown in Figure 1(b), resulting in inefficiency. Therefore, we propose the fundamental question: "Can we directly reconstruct continuous HR signals from LR images?" This serves as the inverse function of imaging process Eq. I, as illustrated in Figure I(c). This approach would not only perform simple sampling to replace multiple upsampling and decoding but also enhance continuity, improving efficiency and performance.
4 PROPOSED METHOD
4.1 CONTINUOUS BASIS FUNCTION
Considering that the target function is continuous, it is crucial to select an appropriate continuous basis function. In this work, we choose the Gaussian function for two main reasons: a) Leveraging the Gaussian Mixture Model (GMM) Reynolds et al. (2009), any complex continuous function can be represented as a combination of several Gaussian functions, ensuring broad applicability and the- oretical soundness. b) With the recent advancements in the Gaussian splatting community Fei et al. (2024); Chen & Wang (2024), the engineering efficiency and compatibility of Gaussian functions have significantly improved, making them highly suitable for practical implementation. Therefore, we use Gaussian functions Gz(r, u) as fundamental continuous functions to reconstruct real 2D continuous signals fa(2, y), , shown in the following equation:
N
fc(z. v) = Σ G,(z,y),
(3)
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Published as a conference paper at ICLR 2026
DDCW
Feature Extractor
FLR
CGM
Differentiable Rendering
APD
LR
Continuous HR
HR
Sampling
Deep Gaussian Prior (DGP)
FIR
Pofs
FLR
Weights
Pentt
Poff
Pfinal
DGP-Driven Covariunce Weighting (DDCW)
Adaptive Position Driftigg (APD)
Figure An overview of the proposed ContinuousSR framework, which consists of three key innovations:
DGP-Driven Covariance Weighting (DDCW), Adaptive Position Drifting (APD), and Color Gaussian Mapping (CGM).
where N denotes the number of Gaussian kernels, and y represents the location in the 2D space. Each Gaussian kernel has eight parameters needed to optimized, which include:
of Oo
po y
Mx My
Cr
Σ =
/ =
Cryb =
Cg
(4)
Cb
where Cryb denotes the RGB parameters of each Gaussian, 1 represents the position parameters, and Σ represents the covariance matrix, resulting in a total of eight parameters be optimized. The value of the Gaussian kernel G; at the position (I, y) can be expressed as:
(-
Gi y, Crgb: Σ) Cryb 2x Σ
exp
(5)
where the distance vector d represents the deviations of x and from their positions fa and Py-
4.2 DIRECT END- TO- AND DEEP GAUSSIAN PRIOR
straightforward approach is to learn the parameters of Gaussian kernels directly from LR images through an end-to-end model. However, this approach is extremely difficult to optimize, as shown in Figure 2(a). As shown in Figure 2(c), the blue loss curve indicates that the optimization process falls into local optimum, with the PSNR remaining as low as 10 dB. To rule out the possibility of coincidence, we conduct multiple experiments and consistently observe the same conclusion.
Why does direct end-to-end fail? We attribute this to main challenges: a) High Complexity: Each kermel in the Gaussian space contains numerous difficult-to-leam parameters that need to be optimized, such as position, covariance, and RGB values. Their domains are unbounded, making the solution space extremely large. For instance, the covariance matrix only needs to be positive definite theoretically. This makes the Gaussian Space significantly larger than traditional image space, while introducing more local traps. This leads to a higher risk of local optima, as illustrated in Figure (a). b) High Sensitivity: Small parameter changes in Gaussian Space can significantly impact the entire image, unlike image space where pixel only affects itself. To further verify this, we add noise with the same distribution both image space and Gaussian Space to evaluate sensitivity. Note that the Gaussian Space is derived through the optimization method Zhang et al. (2024b), denoted vh which requires approximately minute of GPU time per scene, As shown in Figure 2(c), the PSNR drops from 26.31 dB in image space to 13.83 dB in Gaussian Space. This demonstrates that Gaussian Space is much more sensitive, making the optimization more challenging.
Observation and Deep Gaussian Prior. To uncover the secrets of the Gaussian Space, conduct statistical experiments to analyze the distribution of Gaussian parameters. Specifically, we collect and crop approximately 40,000 high-resolution images Timofte et al. (2017); Lim et al. (2017b), and transform them into the Gaussian space using v/. with optimized over 700 GPU-hours. Sub- sequently, statistically analyze the key parameters of Gaussian kernels, including of oi and py. The results, as shown in Figure 2(b), indicate that the distribution of most covariances is traceable: a) Approximately 99% of o2, of and oo fall within the ranges of 0 ~ 2.4, 0 ~ 2.2 and -0.9 1.5, respectively. b) The distributions of the three covariances generally follow
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Published as a conference paper at ICLR 2026
Gaussian distribution. We define this finding as the Deep Gaussian Prior (DGP), which provides valuable information to reduce the difficulty of optimization. Based on these observations, we pro- pose innovative method, ContinuousSR, which for the first time achieves representation learning from low-resolution (IR) images to continuous HR signals. Specifically, ContinuousSR introduces DGP-Driven Covariance Weighting, which simplifies the optimization difficulty in Gaussian Space by constructing pre-defined Gaussian kernels, employing an adaptive weighting mechanism, and incorporating Adaptive Position Drifting based offset drifting. This approach enables superior performance and achieves fast super- reper-resolution results, as shown in Figure and 3.
Disccusion. important note that the Deep Gaussian Prior (DGP) is a fixed statistical prior extracted once from large-scale natural image distributions. Throughout training, this prior remains unchanged- neither fine-tuned nor updated. By serving as stable reference, DGP regular- izes the covariance-learnin solutions. In our experiments, removing the predefined DGP dictionary and directly learning covari- ance parameters resulted in training instability and noticeable degradation in reconstruction quality, underscoring the critical role of DGP in ensuring convergence and robustness. Moreover, to fur- ther assess the generalization capability of DGP across broader scenarios, conducted additional experiments and analyses in Section 5.2.
process and steers the model toward well-conditioned and meaningful
4.3 DGP-DRIVEN COVARIANCE WEIGITING
Directly learning Gaussian covariance parameters remains challenging due their unknown range and sensitive space. Therefore, we propose novel DGP-Driven Covariance Weighting, which leverages the deep Gaussian prior (DGP) to construct a set of pre-defined Gaussian kernels. This ap- proach simplifies the task of directly learning covariance parameters into learning set of weighting coefficients to combine the pre-defined kernels and represent the target kernel, as shown in Figure 3.
Specifically, using the DGP, we sample the three covariance parameters o². of and p from the corresponding distributions of the DGP. These parameters are then used to construct dictionary of N pre-defined Gaussian kernels. The sampling process is expressed as:
oT ~ P(o"), P(o)); Pioi,zoiy ~ p(pozoy),
(6)
to construct pre-defined Gaussian kernels K:
K k= (Gi(
of p
P o.
)
These candidate covariance kernels cover the majority of types and ranges commonly observed in natural images, providing valuable prior information to facilitate network convergence. We then extract features FLR from the input LR image IL using the backbone encoder E. Then, to adaptively generate the target covariance kernel, we introduce an adaptive weighting mechanism that learns a set of weights W [w:),N based on the extracted features. These weights are computed by the adaptive weighting module Mweighh, which operates as follows:
W Softmax PertmaxxMMannn (Meeiga (FLR)).
(8)
The adaptive weighting module M is implemented using several layers of convolutional neural networks (CNNs). Finally, each target kernel is generated by performing a weighted combination of the pre-defined kernels in the dictionary:
N w: G..
Grarget
Through the proposed method, we achieve effective optimization of Gaussian covariance, providing stronger prior knowledge and avoiding the local optima observed method (a), as demonstrated in the ablation study in Section 4.2.
4.4 ADAPTIVE POSITION DRIFTING
The position parameters are also critical for Gaussian kernels, as they determine their locations the 2D space. Directly learning these positions is highly challenging, demonstrated in Section 4.2.

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Published as a conference paper at ICLR 2026
Table 1: PSNR performance comparison with state-of-the-an methods under different benchmarks. Average Time (AT) is reported in milliseconds (ms). The best and the second-best results are in bold and bold. More comparisons are in Appendix Section A.4.
PSNR1
Methods
x4
x6
x8
x10
x12
1
x18
x20
x32
X48
AT
MetaSR
26.76
24.31
22.92
22.02
21.31
20.35
19.96
19.65
18.38
17.48
41.4
LIIF
26.68
24.20
22.79
21.84
21.15
20.19
19.80
19.51
18.30
17.45
110.0
LTE
27.24
24.62
23.17
22.23
21.50
20.47
20.06
19.77
18.47
17.52
151.8
SRNO
26.98
24.43
23.02
22.06
21.36
20.35
19.95
19.67
18.39
17.51
65.7
Urban100
CiaoSR
27.42
24.84
23.34
22.34
21.60
20.54
20.11
19.77
18.45
17.51
341.5
MambaaRR
27.02
24.44
23.01
22.06
21.36
20.34
19.95
19.65
18.29
17.48
90.5
GaussianSR
26.20
23.76
22.35
21.38
20.66
19.68
19.31
19.03
17.86
17.07
321.4
GSASR
27.56
24.94
23.44
22.42
21.68
20.6
20.14
19.81
18.52
17.45
89.1
Ours
27.65
25.02
23.53
22.54
21.79
20.72
20.28
19.93
18.61
17.60
3.3
Ours+
28.22
25.43
23.87
22.86
22.08
20.95
20.54
20,21
18.77
17.70
4.6
MetaSR
29.33
27.03
25.66
24.69
23.94
22.82
22.39
22.01
20.42
19.25
123.5
LIIF
29.27
26.99
25.60
24.63
23.89
22.77
22.34
21.94
20.36
19.19
480.6
LTE
29.50
27.20
25.81
24.84
24.09
22.94
22.50
22.12
20.50
19.31
1407.5
SRNO
29.42
27.12
25.74
24.77
24.03
22.46
22.06
19.27
390.9
CiaoSR
29.59
27.28
25.89
24.91
24.15
22.99
22.54
22.16
20.50
19.30
1857.8
DIV2K
MambaSR
29.36
27.08
25.70
24.74
23.99
22.87
22.44
22.05
20.46
19.27
398.3
GaussianSR
29.03
26.73
25.29
24.23
23.44
22.26
21.81
21.42
19.90
18.76
4962.8
GSASR
29.63
27.30
25.91
24.91
24.14
22.97
22.52
22.13
20.47
19.25
434.1
Ours
29.71
27.40
26.00
25.02
24.25
23.07
22.65
22.25
20.56
19.37
3.5
Ours+
29.80
27.47
26.07
25.08
24.33
23.18
22.74
22.35
20.68
19.45
4.7
MetaSR
26.54
24.64
23.54
22.79
22.24
21.42
21.09
20.80
19.62
18.68
50.4
LIIF
26.49
24.59
23.49
22.75
22.21
21.40
21.09
20.75
19.59
18.65
226.4
LTE
26.73
24.78
23.65
22.88
22.33
21.48
21.15
20.85
19.66
18.71
451.5
SRNO
26.65
24.72
23.61
22.85
22.30
21.45
21.12
20.83
19.64
18.69
163.6
CiaoSR
26.80
24.84
23.69
22.92
22.35
21.48
21.14
20.84
19.63
18.67
1289.3
LSDIR
MamhaSR
26.62
24.69
23.59
22.83
22.28
21.44
21.11
20.82
19.64
18.70
197.7
GaussianSR
26.25
24.39
23.28
22.49
21.92
21.06
20.74
20.45
19.29
18.38
1284.3
GSASR
26.88
24.88
23.73
22.94
22.36
21.49
21.15
20.85
18.62
312.4
Ours
26.95
24.98
23.85
23.06
22.48
21.59
21.29
20.98
19.71
18.75
3.3
Ourst
27.14
25.07
23.91
23.13
22.54
21.69
21.35
21.06
19.79
18.82
4.6
Since each LR pixel typically corresponds to multiple pixels in the HR image, a straightforward solution is fix the positions the centers of the LR pixels. While this strategy simplifies the optimization process, it significantly limits the model's representational capacity, making it difficult to adaptively learn the position distribution based on image content.
To address the above issues, we propose novel method, Adaptive Position Drifting (APD), which not only ensures efficient optimization but also improves representational capacity by allowing the model to adaptively learn positions, as shown in Figure 3. Specifically, we use the center positions of LR pixels as the initialized positions Fmi and further introduce dynamic offset mechanism, which learns dynamic offset from LR features FLR by Mpos model adjust the spatial positions. Here, we set the offset range from -1 by the Tanh activate function and add the offset Pat to the initialized LR center positions to obtain the final positions Pinal, as expressed by the following equation:
(10) where Mpos is implemented using five multilayer perceptron layers. This Pott enables the network to adaptively learn kernel positions based on image content, resulting in denser kernel placement in regions with richer textures and enhancing the network's performance, as demonstrated in Figure 3, Section 5.3 and Appendix Section A.7.
Por (MonhaMp
Pinal Pmit + Pott
In addition, since the RGB is range from O to 1 and is relatively easy to optimize, we introduce a simple Color Gaussian Mapping (CGM) learn the RGB parameters. Specifically, this mapping is implemented using 5 multilayer perceptron (MLP) layers applied to FLR- In summary, the above three components construct our proposed SontinuousSR framework, as shown in Figure 3.
EXPERIMENT AND ANALYSIS
5.1 EXPERIMENT SETTING
Datasets. We use the commonly employed DIV2K and DF2K high-quality dataset Wang et al. (2021) HR images, which are degraded using bicubic to generate LR for training. For evalua-

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Published as a conference paper at ICLR 2026
Table 2: Performance comparison of the Urban 100 benchmark on SSIM, FID, and DISTS metrics.
Metrics
Methods
x4
x6
x8
x10
x
x16
x
x20
x
x48
LIIF
0.7911
0.6861
0.6148
0.5642
0.5270
0.4790
0.4617
0.4503
0.4106
0.3918
LTE
0.8069
0.7045
0.6321
0.5810
0.5422
0.4900
0,4710
0,4588
0,4145
0.3931
GaussianSR
0.7751
0.6633
0.5867
0.5334
0.4967
0.4521
0.4369
0.4277
0.3969
0.3835
SSIM
CiaoSR
0,8110
0.7126
0.6415
0,5887
0,5503
0.4974
0.4777
0,4637
0,4168
0.3921
GSASR
0.8151
0,7153
0.6428
0.5877
0.5483
0,4933
0,4708
0,4562
0.4125
0.3919
Ours
0.8211
0.7250
0.6540
0.6010
0,5600
0,5020
0,4820
0.4680
0,4180
0.3940
Ours+
0.8292
0.7343
0.6624
0.6089
0.5683
0,5097
0.4893
0.4746
0.4216
0.3958
LIIF
0,1611
0.2178
0.2589
0.2926
0.3209
0,3659
0.3835
0.3990
0,4678
0.5322
LTE
0,1570
0.2126
0.2541
0.2872
0,3157
0.3611
0.3799
0.3960
0,4695
0,5362
GaussianSR
0.1740
0.2374
0.2890
0.3296
0.3631
0,4109
0.4302
0.4466
0,5157
0.5713
DISTS!
CiaoSR
0,1533
0.2074
0.2453
0.2771
0.3049
0,3510
0,3701
0,3863
0.4513
0.4998
GSASR
0.1474
0.1996
0.2366
0.2678
0.2957
0,3411
0.3607
0,3784
0.4472
0.4916
0.1415
0.1949
0.2333
0.2640
0.2909
0.3368
0.3556
0.3727
0.4456
0.5160
Ours+
0.1356
0.1901
0.2299
0.2601
0.2860
0,3324
0.3504
0.3670
0.4439
0.5144
LIF
4.76
24.87
50.05
77.54
102.47
145.25
164.41
179.44
256.95
311.09
3.84
21.01
45.15
70.56
92.85
136.54
156.24
170.92
253.52
296.27
GaussianSR
29.30
$7.00
89.25
120.02
166.20
181.59
202.18
264.27
315.97
CiaoSR
3.74
20.48
43.25
58.60
92.58
133.77
151.70
168.89
247.84
294.49
FIDI
GSASR
4.17
22.40
42.19
66.05
87.79
126.72
142.84
161.59
234.27
274.15
Ours
3.06
17.33
39.00
61.73
82,66
122.12
136.84
150.70
227.02
295.34
Ours+
2.91
16.50
37.09
58.83
78.72
116.30
130.32
143.52
216.21
281.27
To आंक
TO आंक
अंंक
आोक
T
क
प बााा
전タ बााा
शााा
पंेे THEN
BANK
bicubic
MetaSR
LIIF
LTE
CiaoSR
लंकक आोक
मीप
आोप
नंका आप
माके शाराा
पसेt बासा
पहिट शा
LR
GT
SRNO
MambaSR
GaussianSR
Ours
bicubic
MetaSR
LIP
LTE
CiaoSR
LR
GT
SRNO
MambaSR
GaussianSR
Ours
Figure 4: Qualitative comparison. The visual quality of method outperforms existing methods. Please zoom in for a better view.
tion. we adopt Set5 Bevilacqua et al. (2012), Set14 Zeyde et al. (2012). B100 Martin et al. (2001), Urban100 Huang et al. (2015), Manga109 Matsui et al. (2017), DIV2K validation Agustsson & Timofte (2017) and LSDIR Li al. (2023b).
Evaluation metrics. Following previous work Chen et al. (2021), we use PSNR, SSIM Wang et al. (2004), FID Heusel et al. (2017), and DISTS Ding et al. (2020) for evaluation. Note that the PSNR/SSIM value is calculated on the RGB channels for the DIV2K validation set and on the Y channel (i.e., luminance) the transformed YCbCr space for the other benchmark test sets.
Implementation details. We conduct experiments under two training settings. First, to ensure fair comparison with prior ASSR methods Chen et al. (2021); Lee & Jin (2022b), we adopt a configu- ration closely aligned with previous works. Specifically,
SwinIR Liang et al. (2021) as the
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Published as a conference paper at ICLR 2026
backbone and train the DIV2K dataset. Ground-trute (GT) images cropped into 256 256 patches, and the corresponding LR images are generated by bicubic downsampling with scale factors sampled from a uniform distribution U (4, 8). The model is optimized with Adam Kingma (2014) starting from a learning rate of 1 x 10-1 decayed by a factor of 0.5 every 100 epochs. We train for 1000 epochs using total batch size of 128 on 8 H20 GPUs with the L.1 loss Chen et al. (2021). Second, to further explore the potential of our approach with a stronger backbone, we conduct ex- periments with HAT Chen al. (2023c). In this setting, we replace the training dataset with DF2K and employ both L1 loss and frequency loss Cui et al. (2023) for supervision, and use the total batch size of 64, while keeping all other training configurations identical. We denote this stronger config- uration as Ours+ in all tables and figures.
Compared methods. We compare with nine state-of-the-ard and popular models: MetaSR Hu et al. (2019), LIF Chen et al. (2021), ) LTE Lee & Jin (2022b), ITSRN Yang et al. (2021), SRNO Wei & Zhang (2023), CiaoSR Cao et al. (2023), MambaSR Yan et al. (2024), GaussianSR Hu et al. (2024) and GSASR Chen et al. (2025). Details are provided in Appendix Section A.
5.2 QUANTITATIVE AND QUALITATIVE RESULTS
Quantitative comparisons. As shown in Table 1 and Table 2, our method achieves the best performance compared to existing approaches across all evaluation metrics and benchmarks. For example, on the Urban 100 dataset, our method surpasses the current state-of-the-ar (GSASR) by 0.09 dB in the x4, representing substantial improvement. Similarly, in terms of SSIM and FID, our method achieves further gains in the x4 scenario, surpassing the current state-of-the-art by 0.0060 and 0.68, respectively. These results demonstrate the effectiveness and superiority of the proposed method.
Complexity comparisons. We present comparisons of runtime and memory usage. Specifically, we evaluate the average runtime across 45 different scales, ranging from x4 to x48. As shown in Table I, our method significantly outperforms existing methods in terms of speed. As described above, the key advantage of our framework is that the continuous high-resolution (HR) signal is generated only once, after which super-resolution at all target scales is achieved through fast rendering. Since methods such MetaSR must regenerate scale-specific features for every target scale, their average runtime is significantly slower. In contrast, our method performs only the lightweight rendering step (approximately ms) for each scale, which leads to the extremely fast average runtime reported. For instance, it surpasses the current state-of-the-arr method (GSASR) by nearly 124 times the DIV2K dataset. Moreover, we also provide comparisons of memory usage. Specifically, we set the input size to 48 48, disable the tiling strategy, and test the memory usage under different scales. As shown in Table 4, thanks to our efficient pipeline design, our method maintains minimal computational overhead across different scales. In contrast, existing INR-based ASSR methods, such as LIIF and CiaoSR, fail to handle larger scales and encounter OOM (out of memory) on V100 GPUs.
Qualitative comparisons. We present qualitative comparisons, as shown in Figure Compared to existing methods, our approach reconstructs more visually pleasing details that are consistent with GT. For instance, in the bottom part of Figure 4, our method effectively reconstructs the texture details inside the building. This highlights the superiority of our method in generating realistic and perceptually satisfying results. More visual comparisons, user studies, benchmark results, FLOPs comparisons, and details are provided in Appendix Sections A.4, A.3, and A.6.
Generalization to
Data. To further evaluate the generalization ability of ContinuousSR beyond natural images, we con- ducted experiments on medical images using the BRATS dataset Menze et al. (2014). The low-resolution inputs were generated through bicubic degradation, and all methods were trained and tested under the same HAT back- bone for fair comparison. As shown in Ta- ble our method maintains strong reconstruc- tion performance even without fine-tuning, indicating good generalization capability to out-of- distribution data.
Table 3: Generalization test of different methods on medical images (BRATS dataset).
x4
x8
PSNR
SSIM
PSNR
SSIM
GSASR Ours
28.02
0.8691
23.38
0.6912 0.7373
29.93
0.8865
25.61

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Published as a conference paper at ICLR 2026
Table 4: Memory usage (G) comparison.
Table 5: Ablation studies.
Memory Usage|
x4
x6
x8
x12
x16
DDCW APD PSNR
Pinn Pan
PSNR
K
PSNR
LIIF
4.12
6.49
9.79
19.27
0OM
10.5
27.8
K1
27.7
CiaoSR
12.17
22.83
00M
00M
0OM
12.3
10.5
K₂
27.1
Ours
2.48
2,49
2.50
2.52
2.54
28.2
28.2
Knee
28.2
5.3 ABLATION STUDY
In this section, we present ablation studies on our proposed APD, DDCW, and DGP using the Urban100 x4 dataset. Specifically, we independently remove APD and DDCW, as illustrated in the right part Table 5. The exclusion these modules significantly exacerbates the optimization difficulty, resulting in considerable decline in PSNR. Then, we validate the effectiveness of Pint and Por in APD. As shown in the middle of Table 5, the results demonstrate that the model achieves the best representational capacity and performance when both components we evaluate the effectiveness separately modify the covariance range to [0.1] and [0,10], using uniform sampling to construct K. and K2. As shown in the right Table 5, incorporating DGP provides a better basis function, thereby enhancing performance. More ablation studies are provided in Appendix Sections A.5.
employed. Finally, DGP in DDCW KDCrP. Specifically, we remove the DGP and
FUTURE WORK
It is well known that, in real-world scenar- ios, image degradation is not limited to low resolution but often includes other types of degradation, such as rain and noise. The goal of low-level vision is remove these degradations while enhancing image resolu- tion and quality. To this end, we evaluate our method on Rain200H Yang et al. (2017), simulating low-resolution rainy images with bicubic downsampling. We compare our ap- proach with three existing wate-of-the-aa methods to validate its effectiveness. As shown in Table 6, our method removes rain degradations more effectively while enhancing resolution and details, out- performing existing methods. This demonstrates the potential of our approach for other low-level vision tasks. In future work, aim to extend it to more tasks to further enhance its applicability.
Table 6: Performance comparison under chal- lenging low-resolution and rainy conditions.
Methods
x4
x5
x6
x7
x
LIIF
24.04
23.53
23.09
22.70
22.39
GaussianSR
24.04
23.51
23.08
22.69
22.38
CiaoSR
23.84
23.45
23.01
22.66
22.33
Ours
24.51
23.95
23.48
23.07
22.76
Considering that the experimental pipeline for DGP is relatively labor-intensive, we will further streamline the overall process, including exploring whether Gaussian regularities can be directly derived from more physical imaging data to simplify the pipeline. In addition, we will also work on further enhancing the network efficiency in future work, making the framework easier deploy across a wider range of real-world applications.
7 CONCLUSION
We introduce ContinuousSR, novel Pixel-to-Gaussian methods designed for fast and high-quality arbitrary-scale super-resolution. By explicitly reconstructing 2D continuous HR signals from LR images using Gaussian Splatting, ContinuousSR significantly improves both efficiency and per- formance. Through statistical analysis, we uncover the Deep Gaussian Prior (DGP) and propose a DGP-driven Covariance Weighting mechanism along with an Adaptive Position Drifting strategy. These innovations improve the quality and fidelity of the reconstructed Gaussian fields. Experiments on seven popular benchmarks demonstrate that our method outperforms state-of-the-arr methods in both quality and speed, achieving 19.5x speed improvement and 0.18dB PSNR improvement, making it promising solution for ASSR tasks.
ACKNOWLEDGMENTS
This work was supported by the Natural Science Foundation of China under Grants 62225207, 62436008 and 62206262.
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A APPENDIX
A.1 DETAILS OF COMPARED METHOSS
To validate the effectiveness of our proposed model, we compare it against seven ofate-of-the-art (SOTA) and widely adopted models: MetaSR Hu et al. (2019). LIIF Chen et al. (2021), LTE Lee & Jin (2022b), SRNO Wei & Zhang (2023), CiaoSR Cao et al. (2023), MambaSR Yan et al. (2024), GaussianSR Hu et al. (2024) and GSASR Chen et al. (2025). For a fair comparison, we select the best-performing networks for each method based on their official GitHub repositories. Specifically, we use the MetaSR model based on SwinIR, the LIIF model based on RDN, the LTE model based on SwinIR, the SRNO model based on RDN, the CiaoSR model based on SwinIR, the MambaSR model based on RDN, and the GaussianSR model on -aaselaseline.
A.2 MORE RELATED WORK
With the rapid advancement of deep learning Yi et al. (2025; 102 Lin et al. (2025c); Gan et al. (2025b); Lu et al. (2024a); Gan et al. (2025a); Hu et al. (2025); Yang et al. (2025c;d;b); Lu et al. (2023; 2024b; 2025); Zhou et al. (2025); Li et al. (2025a): Gao et al. (2024); Lu et al. (2022); Gong et al. (2026; 2024; 2022); Ren et al. (2025); Yang et al. (2025a); Yu et al. (2025); Gao et al. (2025); Zhu et al. (2024); Lin al. (2024b); Zheng al. (2025b;a; 2024; 2023); Lin et al. (2024a); Zheng et al. (2022); Lin et al. (2025b;a); Xu et al. (2025a); Jin et al. (2025); Lan et al. (2025); Wu al. (2025); Zeng et al. (2025); Xu et al. (2025b); Sun et al. (2025); Xu al. (2024); Zhang et al. (2025a;b; 2026); He et al. (2025bxd::;; 2026; 2;25 2024a; 2025a); Xiao et al. (2024); He et al. (2024b; 2023ate;b); Duan et al. (2025a); Xue al. (2025); Duan et al. (2025b); Li Lt al. (2023a; 2024a;b; 2025b;d;e; 2026b;a; 2025e); Gao et al. (2022); Wan et al. (2025), some recent works have also leveraged Fourier-based priors for modeling continuous high-resolution signals Liu & Tang (2025); Akita & Ukita (2025); Zhang et al. (2024a). We initially considered using Fourier basis functions for this purpose. However, the Gaussian Splatting community currently provides more mature tools and efficient rendering pipelines, which makes Gaussian functions more practical and suitable choice for our framework. In the future, we will consider exploring the use of Fourier basis functions in future work to investigate whether they can offer improved performance.
A.3 USER STUDY
To further assess visual quality, we conduct a user study. Ten images are randomly selected from the test datasets, ensuring diversity in image content and complexity. Fifteen participants rate the visual quality of each processed image on a scale from (poor) to 10 (excellent). Each participant evaluates the images independently ensure unbiased results. As shown in Figure 5, the results demonstrate that existing methods frequently fail to restore high-quality images, particularly in challenging re- gions with fine details and textures. This leads to lower user satisfaction, with average scores ranging between 4.2 and 7.0 for most competing methods. In contrast, our method achieves the highest aver- age score of 7.7, significantly outperforming all other approaches. The superior performance of our method demonstrates its ability to produce sharper details, better texture preservation, and visually consistent results. Participants consistently note that our method outperforms others, particularly in challenging regions, further validating its effectiveness and generalization in restoring high-quality images.
A.4 ADDTIONAL COMPARISON
More Benchmarks. To further demonstrate the superiority of our proposed method, we conduct experiments to compare its performance against existing methods using the same SwinIR Liang et al. (2021) backbone on the Set5 Bevilacqua et al. (2012), Set14 Zeyde et al. (2012), B100 Martin et al. (2001), Urban 100 Huang et al. (2015) and Manga109 Matsui et al. (2017) datasets. As shown in Table 7, our method still achieves state-of-the-art performance across all benchmarks and scales. More Comparisons with GSASR. In the main paper, we compared ContinuousSR with GSASR using the performance reported in the GSASR paper under the SwinIR backbone setting (trained on DIV2K only). However, as the GSASR authors have also released an "Ultra Performance" variant, using larger HAT-L backbone and the large-scale SAIB dataset, we further retrained
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Table Performance comparison with existing methods using the same SwinIR Liang et al. (2021)
backbone on the Set5 Bevilacqua et al. (2012), Set14 Zeyde et al. (2012), B 1100 Martin et al. (2001)
and Mangal09 Matsui al. (2017) datasets. Table performance referred to in Cao al. (2023).
Dataset
Scale
SwinIR
MetaSR
LIIF
ITSRN
LTE
CiaoSR
Ours
32.72
32.47
32.73
32.63
32.81
32.84
32.93
x6
29.09
29.46
29.31
29.50
29.62
29.67
Set5
x8
27.02
27.36
27.24
27.35
27.45
27.55
x12
24.82
24.79
24.96
25.18
x4
28.94
28.85
28.98
28.97
29.06
29.08
29.18
X6
26.58
26.82
26.71
26.86
26.88
26.96
Set14
25.09
25.34
25.32
25.42
25.42
25.54
X 12
23.33
23.30
23.38
23.55
X4
27.83
27.75
27.84
27.85
27.86
27.90
27.94
x6
25.94
26.07
26.05
26.09
26.13
26.18
B100
x8
24.87
25.01
24.96
25.03
25.07
25.13
x12
23.59
23.57
23.68
23.76
x4
31.67
31.37
31.71
31.74
31.79
31.91
32.19
27.29
27.69
27.72
27.83
28.01
28.25
Manga109
x8
24.96
25.28
25.23
25.42
25.61
25.80
22.35
22.47
22.79
22.96
Table 8: Comparison PSNR (dB), FLOPs (G), and running time (ms) on the Mangal09 dataset.
x4
X6
x8
PSNR
FLOPs
Times
PSNR
FLOPs
Times
PSNR
FLOPs
Times
SwinIR-LIIF
31.71
365.11
150
27.69
289.77
141
25.28
271.35
137
WrinIR-CiaoSP
31.91
1949.71
319
28.01
1275.95
256
25.61
1048.08
235
SwinIR-GSAB
32.02
395.86
410
28.10
142.51
156
25.69
142.51
156
SwinIR-CCotinuuusSRR
32.19
280.99
136
28.25
125.63
112
25.80
79.21
99
our ContinuousSR under exactly the same Ultra Performance setting for a fairer comparison. Table 11 reports the resulting PSNR values. Under these stronger settings, our method maintains competitive performance relative GSASR's upper bound while offering advantages in efficiency and scalability.
More Comparisons on Real Datasets. To further demonstrate the superiority of the proposed method in real-world scenarios, we compare it with existing methods on the real dataset COZ Fu et al. (2024) on x5. The results, as shown in Table 9, indicate that our method consistently outperforms existing approaches in real-world scenarios, validating the superior generalization ability of our method to real-world data.
More Complexity Comparisons. Furthermore, also provide comparisons of FLOPs and inference time at a single scale on the Mangal09 dataset. Specifically, considering that the image shapes in the original dataset may cause other methods to run of memory, we fix the GT shape to 288 and evaluate the FLOPs and inference time at different seales. As shown in Table 8, our method not only achieves the best performance in terms of PSNR but also maintains the lowest FLOPs and inference time at single scale, significantly outperforming the current SOTA method, GSASR. Note that GSASR pads input images to multiples of 16 before processing, which causes its FLOPs and runtime to remain identical at x6 (LR: 48 x 48) and x8 (LR: 36 x 36). Moreover, Table in the main text further demonstrates our superiority in total runtime across multiple seales. These results fully validate the efficiency and superiority of the proposed method.
More Performance Metrics. In the main text, we have provided PSNR, SSIM, FID, and DISTS metrics to demonstrate the superiority of the proposed method. Here, further present comparison LPIPS performance on the Urban100 4 dataset. As shown in Table 10, our method achieves the best performance in terms of LPIPS. This further validates the superiority of the
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Table Comparison of PSNR on the COZ dataset.
COZ
MetaSR
LIIF
LTT
LINF
SRNO
LIT
CiaoSR
LMI
Ours
PSNR
24.39
24.39
24.4
24.32
24.4
24.36
24.38
24.48
24.68
Table 10: LPIPS.J comparison for Urban 100 dataset across different methods.
LPIPS|
MetaSR
LIIF
MambaSR
LTE
SRNO
CiaoSR
GaussianSR
Ours
Urban100 0. 1989
0.2080
0.2073
O. 0.1934
0.1991
0.1872
0.2285
0.1803
proposed method in perceptual quality. More Compared Method. In the main text, we have compared our proposed method with existing methods to demonstrate its superiority. Additionally, we include a comparison with GaussianImage Zhang et al. (2024b). Specifically, we conduct experiments on the Set5 and Set14 datasets under the x4 scenario. Since Gaussianlmage is an optimization-based end-to-end algorithm, we allow this method to optimize on LR inputs and adjust the Gaussian mapping scale to perform super-resolution for comparison. As shown in Table 18, this method fails to learn the mapping from LR to HR, resulting in poor performance. Furthermore, it is worth noting that GaussianImage requires nearly 1 minute of optimization per scene on V100 GPU, which is impractical for real-world applications.
More Compared Methods with GS. Several recent GS-based ASSR methods have been proposed, such as GaussianSR Hu et al. (2024) and GSASR Chen et al. (2025). GaussianSR has already been thoroughly analyzed and compared in the main text. Here, focus on analyzing and comparing GSASR. Although GSASR has made notable progress, it is still constrained by inefficiencies caused by multiple upsampling and decoding processes across different scales. Furthermore, GSASR performs GS in the feature and image space, which makes it struggle to ensure the continuity contrast, our
of reconstructed images across different scales, leading to low performance. method leverages 2D GS modeling to reconstruct continuous HR images, enabling both fast and high-quality ASSR. Although the GSASR method has not been open-sourced, we still compare our method against the performance reported in its paper. For example, the LSDIR benchmark, method achieves a performance of 27.14 dB at x4, significantly surpassing GSASR's ' best reported performance of 26.73 dB. This demonstrates the superiority of our method in terms of performance. Moreover, in terms of speed, our method requires only ms to generate high-quality HR images across different scales, whereas GSASR takes approximately 91-1573 ms. This further highlights the ultra-fast speed of our proposed method.
More Details in Section 5.2. In Table the Average Time (AT) calculated by performing super-resolution on LR images across 45 different scales, ranging from x4 to x48, and then averaging the total runtime. For each dataset, we select representative LR shape and downsample it by a factor of 48 to construct the input size for each dataset, ensuring that existing ASSR methods do not encounter out-of-memory (OOM) issues. Specifically, the LR size is 21 x 13 for Ur- 100, 42 28 for DIV2K, and 29 19 for LSDIR. As shown in Table 1, our method consistently achieves significant speed advantages over existing methods across different datasets and LR shapes.
A.5 ADDITIONAL ABLATION STUDY
Due to space limitations in the main text, we provide additional ablation experiments demonstrate the effectiveness and rationality of the proposed method. Below, we present detailed descriptions of additional ablation studies and implementation details.
Ablation Study on K. In the DGP-Driven Covariance Weighting, considering the difficulty for deep learning networks to directly interpret the specific meaning of covariance, we map K. from its original three-dimensional representation (i.e., (o o² and p)) to a latent representation space through a convolutional neural network. This approach facilitates better convergence and achieves improved performance. Specifically, explore the performance when the dimension of the latent space is set to 3, 256, and 512, as shown in Table 12. It can be observed that the best performance is achieved when the dimension is set 512, showing significant improvements compared to the
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Table 11: PSNR (dB) comparison between GSASR and Ours under the Ultra Performance setting.
Dataset
Method
x4
x6
x8
x x10
x
x 16
x
x
x32
x48
GSASR
27.17
25.10
23.95
23.13
22.53
21.64
21.28
20.98
19.67
18.61
LSDIR
ContinuousSR
27.23
25.16
24.01
23.21
22.61
21.97
21.42
21.13
19.83
18.86
GSASR
29.82
27.51
26.13
25.14
24,36
23.19
22.73
22,33
20.61
19.35
DIV2K
ContinuousSR
29.95
27.59
26.19
25.19
24.42
23.28
22.83
22.44
20.75
19.51
Bicubic
2u
LIIF
Ave:5.5
LTE
Ave:5.4
SRNO
Ave:6.2
GaussianSR
Ave:6.7
ClaoSR
Ave:7.0
Ours
Ave:7.7
Figure 5: User study,
original direediimesiioal setting. Ablation Study on number of K. Furthermore, we investigate the impact of the number of K. on the network's performance. We define 100, 500, and 730 Gaussian covariances, and the results are presented in Table 13. It can be seen that as the number of covariances increases, the network's 's per- formance improves. However, beyond 730, no further performance gains are observed. Therefore, in this work, set the number of covariances to 730.
Ablation Study on N. Additionally, we study the effect of the number of Gaussians N on the net- work's performance. As described in the main text, initialize Gaussian kernel the center of each LR pixel. We further explore the impact of introducing more Gaussian kernels per unit pixel, and the results shown Table 14. can be observed that the best performance achieved when the number of kernels is set to 4. Introducing too many kernels increases the optimization complex- ity, which does not lead further performance gains. Therefore, we the number of kernels per pixel to 4 in this work.
Ablation Study on Par- Moreover, we investigate the impact and motivation of the Adaptive Posi- tion Drifting (APD) module, which enhances spatial adaptability by allowing controlled positional shifts of Gaussian centers. This design enables the model to better capture local geometric vari- ations and fine details in the image, while maintaining stability through explicitly constrained offset range A, A]. To determine the optimal range, we set A to 0.5, 1, and 2, and report the results in Table 15. It can be observed that setting the range to achieves the best trade-off between representational flexibility and geometric accuracy, whereas both excessively large and small ranges lead to performance degradation.
Ablation Study on the Color Gaussian Mapping (CGM) Module. To provide a more detailed analysis of the Color Gaussian Mapping (CGM) module, we conducted an additional ablation study on its network depth using the Urban100 dataset. The CGM module is designed to learn the color parameters of Gaussian kernels and is implemented as lightweight multilayer perceptron (MIP) that predicts RGB values directly from the encoded feature map. This structure enables the net- work to infer fine-grained color parameters conditioned on both local textures and global context, ensuring accurate and consistent color reconstruction at high scales. We varied the number of MLP layers from to and summarize the results in Table 17. The results show a clear improvement when increasing the depth from 6 layers, while the gain becomes marginal beyond six layers. Therefore, we adopt a six-layer MLP as a balanced configuration between reconstruction quality and computational efficiency.
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Table 12: Ablation on Dim.
Table 13: Number of K,
PSNR
SSIM
PSNR
SSIM
28.14
0.8273
100
28.12
0.8277
256
28.17
0.8281
500
28.19
0.8286
512
28.22
0.8292
730
28.22
0.8292
Table 14: Number of N.
Table 15: Ablation on Par.
PSNR
SSIM
PSNR
SSIM
28.01
0.8254
0.5
28.03
0.8259
4
28.22
0.8292
1
28.22
0.8292
28.18
0.8284
2
28.17
0.8283
Table 16: Performance comparison of SR and deraining methods under different scaling factors.
Types
Methods
x4
x4.5
x5.5
X6
x6.5
x7.5
x8
DRSforere
17.92
16.76
15.84
15.03
14.47
14.02
13.71
13.58
13.50
DRSformer+LIIF
20.14
19.87
19.76
19.53
19.39
19.21
19.12
18.98
18.93
DruSionSRR-DRsoomer
17.14
16.09
15.25
14.53
14.07
13.75
13.53
13.52
Aera+Derain
DRSformer+GGunssinnSS
20.12
19.86
19.75
19.52
19.39
19.21
19.13
18.98
18.94
CiaoRR-DRSformer
19.45
18.25
17.19
16.29
15.55
14.85
14.22
13.94
13.73
DRSformer+CiaSRR
19.96
19.73
19.65
19.48
19.27
19.15
19.07
18.89
18.87
LII
24.04
23.79
23.53
23.29
23.09
22.90
22.70
22.52
22.39
GaussianSR
24.04
23.76
23.51
23.28
23.08
22.89
22.69
22.52
22.38
All in one
CiaoSR
23.84
23.66
23.45
22.23
23.01
22.83
22.66
22.50
22.33
Ours
24.51
24.22
23.95
23.69
23.48
23.28
23.07
22.90
22.76
A.6 ADDITIONAL. VISUAL. COMPARISON RESULTS
In this section, present additional visual comparison results to further demonstrate the superiority of our proposed method, as shown in Figure 7, 8 and 9. can be observed that our method achieves the best visual satisfaction in terms of detailed textures, while also preserving the highest level of detail fidelity, making it closest to the GT image.
A.7 VISUALIZATION OF THE POSITION DISTRIBUTION
To demonstrate the superior adaptive perception capability of the proposed offset mechanism, which effectively introduces more Gaussian kernels in complex texture regions based on image content, we visualize the learned Gaussian position distribution. As shown in Figure 6, the proposed Adaptive Position Drifting adjusts the original initialization of the position distribution by adaptively perceiv- ing the structural content of the image. The results reveal that regions with richer textures have higher densities of Gaussian kernels.
A.8 ALGORITHM WORKFLOW
To clearly demonstrate the details of the proposed method, we design an algorithm workflow, as illustrated in Algorithm This workflow describes the key steps from input to output, including feature encoding, color prediction, offset prediction, covariance estimation, and the final image reconstruction process.
A.9 MORE EXPLORATION AND RESULTS
In Section 6 of the main text, demonstrate the performance of method in low-resolution and rainy scenarios. Here, we present comparisons across more scaling factors and with the two-stage ASSR+Derain methods DRSformer Chen al. (2023b). The results shown in Table 16. As
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PowerPoint 2002
Eveny
Figure 6: Visualization of the position distribution.
Table 17: Ablation Study
CGM Network Depth (Urban 100 dataset).
Number of MLP Layers
FLOPs (G)
Params (M)
PSNR (dB)
4
3.39
0.20
28.12
13.11
0.79
28.19
6
15,80
0.96
28.22
7
22.27
1.35
28.22
26.59
1.62
28.23
shown in Table 16, our method consistently outperforms other methods across all scaling factors. For instance, at the x 4 scale, our method achieves PSNR 24.51, significantly higher than the best two-stage method, DRSformer Chen et al. (2023b)+LIIF (20.14). At the x8 scale, our method achieves 22.76, outperforming DRSformer+LIIF (18.93). Compared to other "All one" methods, approach also achieves superior results, such 23.95 at the x5 scale, outperforming both GaussianSR (23.51) and CiaoSR (23.45). These results highlight the robustness, simplicity, and effectiveness of method for super-resolution and deraining tasks across various scales.
bicubic
LIIF
CiaoSR
GT
SRNO
MambaSR
GaussianSR
Ours
Figure 7: More qualitative comparison. The visual quality of our method outperforms existing methods. Please zoom for a better view.
A. LIMITATION AND FUTURE WORK
Position Distribution. In this paper, to address the difficulty of optimizing position parameters, propose Adaptive Position Drifting, which leverages an offset mechanism to alleviate the optimiza- tion challenges and enhance the representational capacity of the model. However, assigning one or four Gaussian kernels to each LR pixel introduces some limitations. On the one hand it leads overabundance of Gaussian kernels in low-frequency regions, resulting in resource wastage.
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Table 18: Comparison with Gaussianlmage Zhang al. (2024b).
LIIF
LTE
CiaoSR
Gaussianlmage
Ours
Set5 Set14
32.73
32.81
32.84 29.08
28.01 25.62
33.24
28.98
29.06
29.40
Algorithm 1: ContinuousSR.
Input: inp: (B, 3, Hg. W
Output: image (B, 3, H. W)
F & (ncoder(inp);
C CGM(F) // [N 3]
Az <- APD(F) /// [N
DDCW [F) : // [N 3
< Grid(H, W) + aAx
7I E N do (zys,, depth,, radii,, conic,) < Project(ăn, En);
image + Composite (wzys depth, radii, conic, C));
On the other hand, increases the optimization difficulty significantly. To address these issues, we plan to explore the adaptive allocation of Gaussian kernels based on the texture complexity of image content in future work. This approach aims to dynamically assign an appropriate number of kernels to different regions, effectively mitigating the aforementioned problems.
Introduce Generation Knowledge. In addition, considering that arbitrary-scail super-resolution sometimes requires large upscaling factors (e.g.. x16, x32, etc.), it is challenging for the model to generate high-quality details solely relying on the input image and model knowledge. Therefore, in the future, we plan to incorporate more visual knowledge from diffusion models semantic knowledge from large vision-language models to help the network generate finer details for high- magnification scenarios.
A.11 USE OF LARGE LANGUAGE MODELS
Large Language Models (LLMM)) were used only for grammar checking and text polishing. All research ideas, methods, and analyses are solely by the authors.
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bicubic
MetaSR
LIIF
LTE
CiaoSR
LR
GT
SRNO
MambaSR
GaussianSR
Ours
Figure 8: More qualitative comparison. methods. Please zoom in for better view.
The visual quality of our method outperforms existing
bicubie
MetsS
CiaoSR
SRNO
MambuSR
GaussinoSR
Ours
Figure 9: More qualitative comparison. The visual quality of our method outperforms existing methods. Please zoom in for a better view.
28


Reviews:
[{"review_id": "oGLVgnmRMI", "reviewer": "Reviewer_hqg6", "summary": "This paper introduces ContinuousSR, an approach to arbitrary-scale super-resolution (ASSR) that reconstructs continuous high-resolution (HR) signals from low-resolution (LR) inputs using 2D Gaussian Splatting. The core idea is a Pixel-to-Gaussian paradigm: instead of repeatedly upsampling and decoding features as in coordinate-based implicit neural representations (e.g., LIIF, CiaoSR), the method builds a single continuous Gaussian field from which HR images at arbitrary scales can be rendered in ~1 ms per scale.\n\nThe authors propose three technical components:\n\n1. Deep Gaussian Prior (DGP) – a statistical observation that Gaussian parameters from natural images follow a bounded Gaussian distribution.\n\n2. DGP-Driven Covariance Weighting (DDCW) – a mechanism that learns adaptive weights over a pre-sampled dictionary of Gaussian kernels to stabilize training.\n\n3. Adaptive Position Drifting (APD) – a bounded offset learning scheme that adjusts Gaussian centers based on image content to refine spatial alignment.\n\nEvaluated on seven benchmarks (Set5, Set14, B100, Urban100, Manga109, DIV2K, LSDIR) with SwinIR and HAT backbones, ContinuousSR achieves slightly higher PSNR (up to +0.18 dB on Manga109) and SSIM than state-of-the-art GSASR, while being up to 19.5× faster when rendering across 40 scales. Memory usage is also markedly lower, enabling high-scale rendering (×32–×48) where prior INR methods run out of memory.", "questions": "Q1. Novelty vs Prior Work: How does ContinuousSR fundamentally differ from GaussianSR (which already models each pixel as a Gaussian field) and GSASR (which performs scale-aware 2D Gaussian Splatting)? Beyond eliminating per-scale decoding, what conceptual innovation justifies a new paradigm claim?\n\nQ2. Can you clarify whether DGP is fixed or fine-tuned during training? How would results change if covariances were learned end-to-end without the predefined dictionary?\n\nQ3: How is the Color Gaussian Mapping (CGM) implemented and how crucial is it for final image quality? For instance, do you simply assign each Gaussian an RGB value from the LR feature map, or is there an additional refinement to predict high-frequency color details? A clearer explanation of CGM’s role would help understand how color fidelity is maintained for large upscaling factors.\n\nQ4: Does each input pixel strictly correspond to one Gaussian, or can multiple Gaussians represent a single pixel/region? The current description implies one Gaussian per LR pixel. If so, have you considered allowing a more adaptive number of Gaussians (e.g., splitting a pixel’s Gaussian to capture complex textures, or merging in flat areas)? This might further improve representation capability for highly textured regions.\n\nQ5: How sensitive is the model to the assumed Deep Gaussian Prior if applied to very different data distributions? For example, would a model trained on natural images struggle with domain-specific content (such as medical images or line drawings) because the covariance distributions differ? Would fine-tuning the DGP (or the predefined kernel set) be necessary in such cases?\n\nQ6: What measures are in place to ensure that Adaptive Position Drifting does not distort the image structure? Since APD shifts Gaussian centers based on content, do you impose any regularization or limits on these offsets? Clarification on how much drift is typically learned (e.g., fraction of a pixel) and its effect on alignment with ground-truth details would be helpful.\n\nQ7: Can you elaborate on the rendering performance in extreme cases? The reported 1 ms rendering is impressive – what output resolution and hardware does this refer to? If one were to render an extremely high-resolution image (say 4K or 8K) from a small LR input, does the rendering remain near-instantaneous, and are there any memory or precision considerations with the GPU rasterizer at those scales?\n\nQ8. Reproducibility: Will you release the code, pretrained models, and the 40 000-image covariance statistics used to construct the DGP?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "Ultra-Fast Arbitrary Scaling: The proposed method eliminates iterative upsampling, achieving real-time rendering at arbitrary scales (≈1 ms per scale after one forward pass). This is a ~20× speedup over implicit models when generating a continuous zoom or multiple outputs, making it highly attractive for practical use where efficiency is crucial.\n\nHigh Reconstruction Quality: It delivers superior super-resolution fidelity across a wide range of scales. On multiple benchmarks (e.g. Urban100, Manga109), the approach surpasses previous state-of-the-art methods by a significant margin (~0.8–0.9 dB PSNR improvement at 4× upsampling), and also achieves better SSIM and FID scores, indicating improvements in perceptual quality. The Gaussian representation better preserves structure and details (sharper textures, as shown in qualitative examples) compared to coordinate-MLP approaches.\n\nInnovative Use of Gaussian Splatting with Deep Prior: The paper cleverly integrates Gaussian splatting (recently popular in 3D/NeRF tasks) into image SR, and addresses its training difficulties via a Deep Gaussian Prior. By pre-characterizing the typical covariance range of natural images, the method avoids converging to poor local optima and reduces the solution space. This DGP-driven covariance weighting mechanism and the adaptive position drifting are novel contributions that improve modeling flexibility (allowing anisotropic Gaussian shapes and content-aware placement) while keeping the optimization stable.\n\nExcellent Memory Efficiency: The continuous Gaussian framework uses memory proportional to the number of Gaussians (which is tied to input resolution) rather than the output pixel count. Consequently, the method can handle very large scale factors (e.g. 16×, 32×) without running out of memory, unlike prior methods (LIIF, CiaoSR) that exhaust GPU memory at high scales. This efficient scaling is beneficial for ultra-high-resolution outputs and demonstrates a well-designed pipeline.\n\nThorough Evaluation: The authors validate their approach on seven diverse datasets and include extensive comparisons to nine prior methods (including recent ones like GaussianSR and GSASR). They also perform ablation studies to justify each component, and even test a joint deraining+SR scenario to show the model’s robustness in adverse conditions. This thorough experimentation strengthens confidence in the method’s effectiveness and generality.", "weaknesses": "Complex Training Pipeline: The solution introduces multiple components (DGP-based kernel dictionary, adaptive weighting network, position drift module, etc.), making the overall pipeline more complex than some prior approaches. Training requires careful setup – for instance, deriving the DGP involved 700 GPU-hours of optimization on external data, and the model must learn to combine predefined kernels and offsets correctly. This complexity might make the approach harder to reproduce or extend without the provided code, and it suggests a heavy computational cost in pre-processing and training (though inference is efficient).\n\nHeavily Engineered Solution: While effective, the novelty is somewhat incremental in that it builds upon known ideas from related domains (e.g. using Gaussian splats instead of coordinate MLPs, which was explored by GaussianSR and GSASR). The main contributions lie in engineering a workable solution (using a learned prior and adaptive modules) rather than fundamentally new theory. The approach may be viewed as a clever combination of existing techniques (INR networks, Gaussian rendering, learned priors) tailored to overcome a training hurdle, which, despite being valuable, might not be conceptually groundbreaking.\n\nAssumptions of the Deep Gaussian Prior: The method’s efficacy leans on the assumption that natural image content adheres to the learned Gaussian parameter distributions. If an input deviates from this prior (e.g. very sparse or non-photographic images), the fixed kernel dictionary might become suboptimal. The paper does not explore how sensitive the model is to the DGP assumptions or how it would perform on out-of-distribution data (such as medical imagery or vector graphics). In other words, the generalization of the DGP to all image types remains a potential concern.\n\nLimited Discussion of CGM and Color Fidelity: The Color Gaussian Mapping component is not described in detail, leaving some ambiguity about how color from the LR image is transferred or refined in the continuous representation. If color assignment to Gaussians is naive (e.g. directly using LR pixel colors), it could limit the method’s ability to add high-frequency color details. More explanation is needed on how color is handled and whether the model can recover color nuances at high scales. This lack of clarity is a minor presentation issue but also a technical point that could affect the perceived color fidelity of results.\n\nPotential Minor Artifacts: Representing an image as a sum of Gaussians could inherently introduce smoothing, especially if a Gaussian’s covariance is large. The paper focuses on PSNR/SSIM, which favor fidelity, but it’s unclear if there are any artifacts such as slight blurring or spatial shifts due to APD. For example, Adaptive Position Drifting moves Gaussian centers for better alignment; however, without constraints, this might risk slight geometric distortions (if many Gaussians shift from their original pixel locations). The authors do not report any artifacts, but a discussion on how APD balances flexibility with spatial accuracy would strengthen the work.\n\nMarginal quantitative gains: The reported PSNR improvements over GSASR (≈0.08–0.18 dB) are small; hence claims of a “new paradigm” feel overstated given access to those prior results.\n\nAblation gaps: Table 4 compares DGP to uniform sampling but omits a baseline with directly learned covariances (no dictionary), leaving uncertainty about DDPW’s absolute necessity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lzyrktW1Vu", "reviewer": "Reviewer_mpzJ", "summary": "This paper proposes ContinuousSR, a novel framework for arbitrary-scale super-resolution that leverages 2D Gaussian modeling to directly reconstruct continuous high-resolution signals from low-resolution images in a single pass. By introducing the Deep Gaussian Prior (DGP) and key modules such as DGP-Driven Covariance Weighting and Adaptive Position Drifting, the method achieves both high-quality reconstruction and ultra-fast rendering speed, outperforming state-of-the-art methods in both efficiency and performance across multiple benchmarks.", "questions": "1. What specific sampling strategy was used to draw Gaussian covariances from the DGP in Eq. (6)? \n2. Each low-resolution pixel is assigned four Gaussian kernels, but the reason for this specific choice is unclear. Why are four Gaussian kernels assigned per LR pixel? \n3. The predefined Gaussian kernel dictionary plays a key role in covariance weighting, yet its size and the rationale behind it are not specified. How large is the predefined Gaussian kernel dictionary, and how was this number determined?\n4. The model is trained on data generated by bicubic downsampling, and the evaluation is also conducted on synthetic datasets created in the same way. Has the performance of ContinuousSR been tested on real-world arbitrary-scale super-resolution datasets such as COZ[1]?\n\n[1] Fu, Huiyuan, et al. \"Continuous optical zooming: A benchmark for arbitrary-scale image super-resolution in real world.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. The paper derives a Deep Gaussian Prior (DGP) from large-scale natural image statistics, effectively addressing local minima in Gaussian kernel optimization and proposing a novel continuous modeling framework for arbitrary-scale super-resolution.\n2. Extensive experiments on multiple benchmarks and scales, evaluated by PSNR, SSIM, FID, and DISTS, clearly verify the effectiveness of ContinuousSR and show its potential in other low-level vision tasks such as deraining.\n3. The method fully utilizes the continuity of Gaussian kernels, achieving one-pass generation and multi-scale rendering with high efficiency and speed.", "weaknesses": "1. A spelling error appears on line 175 of the paper: “the theresponding discrete pixel grids” should be “the corresponding discrete pixel grids.”\n2. The paper leaves several important implementation details insufficiently described. In particular, the sampling strategy used to generate Gaussian covariances from the Deep Gaussian Prior and the construction of the predefined Gaussian kernel dictionary are not clearly explained. Without clarification of how the sampling and dictionary size were determined, it is difficult to assess the reproducibility and design rationale of these components.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "h7oGmp1XHT", "reviewer": "Reviewer_Jk3f", "summary": "1. This paper proposes ContinuousSR, a Pixel-to-Gaussian paradigm that reconstructs a continuous 2D high-resolution (HR) signal (a Gaussian field) from a low-resolution (LR) image in a single pass, enabling fast arbitrary-scale rendering via Gaussian rendering\n2. The paper identifies a Deep Gaussian Prior (DGP) on Gaussian covariance parameters from statistics over ~40k images and uses it to constrain the covariance space.", "questions": "1. In Figure 3 you mention using differentiable rendering to render the HR image from a set of Gaussians. Are you using the 3DGS rendering pipeline? If so, I think the GMM-related theory in Eq. (3) may be inappropriate, because 3DGS rasterization is based on alpha-blending theory rather than GMM theory. If not, how is your differentiable rendering implemented, and how does it match your GMM formulation?\n\n2. Since there is a “Deep Gaussian Prior,” is there also a “Deep Fourier Prior”? Is it possible to simply replace Gaussians with Fourier bases of different frequencies and linearly combine them to obtain the final HR image? Using Fourier bases to describe the composition of a continuous HR function may be more in line with traditional intuition. I recall some works a few years ago that applied similar ideas to arbitrary-scale super-resolution, but I forgot the names; it would be better to add them in the related work section.\n\n3. I think the Gaussian rendering parts can be further refined. For example, the theory and methods of “Mip-Splatting” (CVPR 2024) might be applicable to your current framework. I’d like to hear your thoughts on this.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Combining ideas from 3D Gaussian Splatting to model continuous 2D image signals is very interesting, and the paper convincingly demonstrates the effectiveness of this idea。\n2. Strong empirical efficiency: consistent large speedups, stable memory across scales, and favorable FLOPs/runtime on Manga109 single-scale tests and multi-scale averages.\n3. The upsampling/rendering component is an explicit, trainable parameter-free module at inference, which is both elegant and practical.", "weaknesses": "1. I think the theoretical connection between GMM-based formulation and the actual HR rendering needs clarification (see Question 1).\n2. Dependence on the prior dictionary: performance hinges on the size and sampling of the DGP-derived kernel dictionary; generalization to out-of-distribution (OOD) content or non-natural images is unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kpErriyPxE", "reviewer": "Reviewer_wJ6J", "summary": "This paper introduces a 2D Gaussian splatting based arbitrary-scale SR model. With the help of Deep Gaussian Prior and Adaptive Position Drifting, the proposed ContinuousSR achieves excellent performance and ultra-fast inference speed.", "questions": "This paper proposed a novel and valuable method in ASSR task. While I am going to give weak accept towards the whole quality of the paper, I hope the author could provide some reliable answers to the weakness of the paper. I am willing to increase the rating score if the author can address the questions I raised in a satisfactory manner.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1.\tThe author organized a soundly description about the motivation, the detailed technique and experimental implementations, which makes the method looks valuable especially when implementing the method into industrial applications. \n\n2.\tThe DGP proposed in this paper appears to be quite novel and reliable, effectively capturing the prior information of images, which indeed helps the model generalize across images with different content.\n\n3.\tThe author provides large quantities of experiments to validate the effectiveness of the proposed method. The results reported in the paper also seem highly competitive in the current super-resolution field, where performance is approaching saturation.", "weaknesses": "1.\tPlease unify the style of the references. The authors neglected to maintain consistency in the references regarding conference name capitalization, full conference names, abbreviated conference names, and title capitalization. The ununified samples in the paper are listed in the following, \n\n“Xiaoyi Liu and Hao Tang. Difffno: Diffusion fourier neural operator. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 150–160, 2025. 2, 3” \n\n“Jaewon Lee and Kyong Hwan Jin. Local texture estimator for implicit representation function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1929–1938, 2022a. 2, 4”\n\n2.\tThe inference time in Table.1 seems not soundly. From my point of view, the author seems not to indicate the acceleration strategy in the paper, then how could ContinuousSR runs even much faster than the very lightweight model Meta-SR?\n\n3.\tPlease indicate the detailed settings in training progress, especially the data preparation. The author says they fix the GT size to 256, while randomly sampling the scaling factor, then if want to do parallel training, in the code implementation, the author must pad the LR image to a fixed size. However, in this way, it will waste huge computational resources since the padding area will introduce additional computational burden. What’s more, why not adopting the same strategy in most the existing method? They just fix the LR image size while randomly sampling the scaling factor.\n\nMinor:\n\n1.\tIs there any GPU memory usage comparison results with state-of-the-art models?\n\n2.\tAlthough different ASSR models might adopt different training settings, but I hope the author could provide some fair experimental settings, such as the fixed LR size, the same scaling factors (1, 4).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)