Learning Heterogeneous Degradation Representation for Real-World Super-Resolution
Authors: Haowei Li
Pengxu Wei
Dongyu Zhang
Liang Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Real-World Super-Resolution
Representation Learning.
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Design a discriminative  and interpretable per-pixel  representation to handle real-world heterogeneous degradations.

Abstract: Real-World Super-Resolution (RWSR) aims to reconstruct high-resolution images from low-resolution inputs captured under complex, real-life conditions, where diverse distortions result in significant degradation heterogeneity. Many methods rely on degradation representations, yet they struggle with the lack of spatially variant degradation modeling and degradation-content entanglement. We propose Spatially Amortized Variational Learning (SAVL), an implicit framework that models per-pixel degradations as spatially varying Gaussians inferred from local neighborhoods. SAVL couples a conditional likelihood lane (SAVL-LM) with a mutual information suppression lane (SAVL-MIS) to filter out degradation-irrelevant signals, yielding a well-constrained solution space. Both our qualitative visualizations and quantitative analyses confirm that the learned representations effectively capture the spatial distribution of complex degradations while being highly discriminative of diverse underlying degradation factors. Building on these representations, we design a degradation-aware SR network with channel-wise guidance and spatial attention modulation for adaptive reconstruction under heterogeneous degradations. Extensive experiments on real-world datasets demonstrate consistent gains over prior methods.

Published as a conference paper at ICLR 2026
LEARNING HETEROGENEOUS DEGRADATION REPRE- SENTATION FOR
SUPER- RESOLUTION
Haowei Li', Pengxu Wei!! Dongyu Zhang', Liang Lin
1 "Scoool of Computer Science and Engineering, Sun Yat-sen University ?engg Cheng Laboratory 1ihw5 whww59mmail sysu. edu. en, {weipx3, zhangdy27] ( @maii sysu. edu. en, 1 Anllann@ieeee
ABSTRACT
Real- World Super-Resolute (RWSR) aims reconstruct high-resolution images from low-resolution inputs captured under complex, real-life conditions, where diverse distortions result in significant degradation heterogeneity. Many methods rely on degradation representations, yet they struggle with the lack of spatially variant degradation modeling and degradation-conten entanglement. We pro- pose Spatially Amortized Variational Learning (SAVL), implicit framework that models per-pixel degradations as spatially varying Gaussians inferred from local neighborhoods. SAVL. couples conditional likelihood lane (SAVI--LM) with a mutual information suppression lane (SAVL-MIS) to filter out degradation- irrelevant signals, yielding well-constrained solution space. Both our qualitative visualizations and quantitative analyses confirm that the learned representations effectively capture the spatial distribution of complex degradations while being highly discriminative of diverse underlying degradation factors. Building on these representations, we design a degradation-awan
SR network with channel-wise guidance and spatial attention modulation for adaptive reconstruction under het- erogeneous degradations. Extensive experiments on real-world datasets demon- strate consistent gains over prior methods.
INTRODUCTION
Single Image Rupee-Resolution (SISR) aims to reconstruct high-resolution image from a single low-resolution input by recovering lost high-freuuecc details and structural information. Tradi- tional SISR methods often rely on fixed and idealized degradation model, most commonly bicubic downsampling Dong et al. (2015); Ledig et al. 2017); Lim et al. (2017); Wang et al. (201 8b); [Zhang
al. (2018c Sc). However, this assumption fails to capture the heterogeneous degradations present in real- world scenarios, leading to significant performance drops. Real-world image degradations are substantially heterogeneous, varying both between images and spatially within a single image. This heterogeneity stems from wide array of factors, including differences in imaging devices Ig- natov et al. (2017): y Wei et al.] [2000]] Xu et al. (2022), varying capture conditions (Aakebberg et al. (2024) (e.g., ISO), and diverse ISP pipelines, as well as intrinsic image properties like DoF and tex- ture complexity Kim et al. (2021) Liang et al. 2022bb: Chen et al. (2023b) that introduce spatially non-uniform degradations.
To tackle complex real-world degradations, many works learn degradation representation to guide the image upsampling process. These approaches can be broadly categorized into two main paradigms: explicit degradation estimation and implicit degradation learning. Explicit degradation estimation Gu al. (2019): [iang et a (202Tb): Huang al. (2020); [Liang et al. (002)) operates by defining parametric degradation space. These methods typically synthesize data with a fixed degra- dation pipeline and then supervise an estimator to predict specific degradation parameters (e.g., blur kernel, noise level). However, the reliance a predefined and often simplistic degradation space limits their applicability to weal-world scenarios, where degradations are more complex and deviate from the training assumptions. In contrast, implicit degradation learning aims to construct latent
"Corresponding author.

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Published as a conference paper at ICLR 2026
LightBSR
Ours
Figure 1:
Visualization of Degradation Representations. Compared to LightBSR Yuan al. (2025).
our learned representations form well-separated clusters for distinct degradation factors (ISO, focal length, sensor, and texture). This demonstrates a superior ability to discriminate between fine- grained degradation variations caused by these factors.
space for an Implicit Degradation Representation (IDR) that can generalize to unknown and com- plex degradations. Rather than predicting fixed parameters, these methods employ techniques such
as meta-learning Xia et al. 2023), contrastive learning Wang et al. (2021a); Mou et al. (2022): )
Lin
ct al. (2024a). knowledge distillation Xia al. (2022) Liu et al. (004 Yuan et al. (2025) to
learn deterministic IDRs that encode diverse degradation patterns.
Existing Implicit Degradation Representations (IDRs) have two major shortcomings: (1) Lack of modeling for spatially variant degradations. They often fail to account for the fact that real-world complex degradations are not uniformly distributed within an image. (2) Insufficient decoupling of degradation from content. Unlike the constrained parametric spaces of explicit estimation, the latent space for IDRs is complex and unconstrained. This high capacity allows weakly regularized embed- dings to be overly expressive, easily encoding the entire LR signal, including degradation irreleeant content (c.g.. appcarance. semantics) Li et al. 2024); Yuan al.) (2024). This degradation- content entanglement renders the representation non-diseriminative for degradation, failing pro- vide useful guidance for the SR process and thus impairing generalization. For instance, shown in Fig. representations from LightBSR Yuan et al. (2025) show significant overlap across different degradation factors, whereas our learned representa- tions form distinct clusters corresponding factors such focal length, ISO, sensor, and texture.
CDFomer
LigHRSR
x
MINE(rep, degradation)
KDSR
X
20-
More Degradation-Coree
MANe
x
20
Less Content-Correlant
DASR
12
1B 6a
as
MINE(rep, content)
Figure 2: Correlation of IDR with Degrada- tion and Content. IDRs are mapped by corre- lation with degradation factors (Y-axis) and image content (X-axis): explicit represen- tations (bottom-right) show low correlation with both, while conventional implicit ones (top-left) are highly correlated with both, re- vealing strong degradation-comm contentt entangle- ment.
The challenge of degradation-content entangle- ment Wei et al.] (2023b); Guo et al.) (2024b); Wei et al. (2023a) is magnified when modeling spatially variant degradations. A per-pixel latent space is in- herently more complex and, without effective regularization, becomes more prone to encoding con- tent. Moreover, the assumption of spatial variance directly undermines contrastive learning meth-
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Published as a conference paper at ICLR 2026
ods Yuan et al. (2024). their core assumption that different patches from the same image share the same degradation and thus form positive pairs no longer holds.
To address these challenges, we introduce SAVL, Spatially Amortized Variational Learning frame- work. SAVL models per-pixel degradations as spatially varying Gaussian distributions inferred from local image neighborhoods. The Gaussian posteriors inherently capture the spatial non-uniformity of degradation. To decouple the representation from content, we enforce a whitening prior and apply an explicit mutual information constraint. We leverage an amortized inference network efficiently predict the local posteriors, thereby avoiding the prohibitive computational cost of per-pixel opti- mization Guo et al. (2024a). For each pixel, the predicted posterior provides a mode and a variance; the mode characterizes the degradation type, while the variance quantifies its uncertainty or sever- ity, yielding an interpretable representation. This dual-component representation enables powerful guidance mechanism: the mode provides channel-wise modulation, and the variance provides spa- tial modulation for the downstream SR network. Our approach achieves state-of-the th-th--rr results on both synthetic and real-world datasets. Fig. 2 visualizes the correlation of various degradation representations with both degradation factors and image content, quantified via Mutual Information Neural Estimation (MINE) Belghazi et al., (2018) (details in Sec. 4.2). The analysis reveals clear trade-off. Explicit representations, found in the bottom-right quadrant, successfully suppress con- tent correlation due their constrained parametric nature, but at the cost of insufficient degradation modeling capability. Conversely, existing implicit representations populate the top-left quadrant, exhibiting a strong correlation with both axes and thus indicating severe degradation-content entan- glement. In contrast, our method is the only approach that learns an implicit representation with strong degradation correlation while simultancously minimizing content correlation. In summary, our main contributions are as follows:
Spatial heterogeneity modeling. We introduce SAVL, a framework that learns a spa- tially varying Gaussian posterior for each pixel from its local neighborhood. This method achieves stable, spatially-resolver degradation representation in single, efficient pass.
Suppressing degradation suppression term into the conditional ELBO, which explicitly filters enggradationiirrleee content. The result is per-pixel representation that is both well-constrained and highly discriminative the underlying degradation.
entanglement. We incorporate nntuullinffrmmation
Degradation-awar SR integration. We propose novel mechanism that injects our rep- resentation into an SR backbone using both components of the learned posterior: the mode provides Channel-wise guidance, and the variance enables spatial feature modulation. This strategy delivers consistent performance gains across synthetic and real-world benchmarks.
2
RELATED WORK
World Super-Resolution. Traditional SR methods often assume a simplistic degradation like bicubic downsampling Dong et al. (2015); Ledig et al. (2017). hindering their performance real-world images. While datasets with realistic paired images like RealSR CCa et al. (2019) and DRealSR Wei et al. (220)) have been developed to mitigate this, major research direction involves explicitly modeling the complex degradations. In the non-blind setting, methods like SRMD Zhang al. (2018a), UDVD Xu et al.] (2020), and DPSR Zhang et al. (2019) take known degradation parameters as input. For the more challenging blind setting, numerous approaches estimate these parameters first. These include supervised estimation of global degradations Gu et al. (019)) Liang et al. 2222bb 2022), estimation of spatially varying degradation Kim et al.J (2021): Chen et al. (2023b); Guo et al. 2024a). and joint optimization of the estimator and the SR network Huang al. 2020); Cai et al. (2019) Guo et al. (2024a). Despite their progress, all of these explicit estima- tion strategies are confined to a predefined degradation space. This limits their generalization, as real-world degradations are often more complex and unknown at training time, posing significant challenge to existing solutions.
Implicit Degradation Representation Learning. Numerous methods utilize implicit degradation representation learning to guide the SR process. Foundational approaches established various learn- ing paradigms to obtain supervised self- self-supervised signals, including contrastive learning Wang et al. (2021a), knowledge distillation Xia et al.] (2022); Yuan et al.] (2025). and metric learning Mou al. 20022 Subsequent works have focused refining these representations, for instance, by
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Sputially Amoriized Variational Learning
L1 Loxs
Amortized Estimator Pu(rib)
Amortized Estimator
Pr(
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Par-piucl Kiausiaa Cormen
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Figure 3: The main pipeline of our method.
modeling them as probability distributions suppress uncertainty Lin al. (2024a) by leverag-
ing powerful generative ability like diffusion models Liu et al. (2024). A central challenge in this degradation and content features. To address this, some methods
domain is the entanglement propose explicit decoupling mechanisms, such as cyclic shift sampling Yuan et al. (2024). Another challenge to learn spatially variant degradation from real data. Aakerberg al. (2024) learns pixel-wise degradation feature, though simple additive formulations without strong priors risk con- tent leakage into the representation. Despite this progress, a fundamental tension remains: the push towards modeling spatially variant degradations requires more complex, per-pixel latent space, which in turn makes the goal of robustly decoupling degradation from content features significantly more challenging.
SPATIALLY AMORTIZED VARIATIONAL LEARNING
Overview. We present SAVL (Spatially Amortized Variational Learning) to learn heterogeneous degradations. SAVL learns degradations implicitly: each pixel-wise degradation variable modeled spatially varying Gaussian whose posterior is inferred from local evidence with parameters shared across space and images. The framework has two lanes that are estimated by the same amor- tized networks and composed the end. SAVL-LM learns spatially factorized conditional like- lihood via a conditional ELBO. SAVL-MIS implements a mutual- -intouaaiiooo suppression strategy that upper-bounds and penalizes (r::), ensuring that the learned degradation distribution captures degradation-speecfic factors rather than content. Sharing estimators across lanes yields a two-term loss and single-pass inference. As shown in Fig. 3 SAVL and the SR network are jointly trained at first. The learned representations are directly injected into the SR network for degradation-aware super-resolution. We insert our Degradation- Guiiddd Spatial and Channel Attention (DSCA) mod- ule illustrated in Fig. 4 before Hybrid Attention Blocks (HAB) and Overlapping Aatss-Attention (OCAB) Chen et al. (2023a), serving as the initial stage of every Degradation-Awil Aware Residual Hy- brid Attention Group (DA-RHAG). Once converged, Amortized Estimators are frozen for down- stream SR tasks. More details are provided in Section 3.4
3.1 SETUP AND ASSUMPTIONS
Let x be a clean high-resolutin image and y the observed low-resolution image. Let D denote a deterministic downsampling operator (e.g., bicubic), and define the content code D(x). Let c z² index pixels, and let MC(u) be the Chebyshev neighborhood pixel u with radius s. Let r(:) (r(u) E Rmy Jenn denote spatially varying degradation field.

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Learning objective from paired data. We learn degradations from paired real data (z, y), can optimize the conditional marginal likelihood of y given under the data distribution Paau (z, y) To prevent content leakage into the degradation variables, we constrain the mutual information be- tween r and 2:
max E Ppan(x.)) log py []]
po
s.t. T(r;2) S
2(); is taken under (r, 2) Pdat (xr, y) q(r y) with (xx)). We collect all parameters e.
Using a Lagrange multiplier 2 0, we obtain the penalized objective
A)) pp log py [=]] AI(r;)),
where the additive constant As is dropped since does not depend on O. We address the two terms in this penalized objective separately. SAVL-LM maximizes tractable lower bound the log- likelihood term, while SAVL-MIS minimizes a tractable upper bound on the mutual information
term.
Assumptions. (A1) Spatial amortization. Both the likelihood (v | 2,r) and the variational posterior (r y) implemented using freetive-field-limieee networks Liang al. (202Tb) with parameters shared across pixels and images, yielding amortized inference that replaces costly per-image and per-pixel optimization Guo al. (2024a) with a learned single-pass mapping. This design suits real-world degradations: optical effects, sensor noise, and compression act locally and vary smoothly, so shared local modules capture common rule across space and images, reducing variance and parameter count. (A2) Local conditional independence. Given (2, pixels are conditionally independent and each pixel depends only on local content and local degradation:
po 2,,)
II pel (u)) =(N(u)) r(w)). uef
33)
(A3) Mean-field Gaussian posterior and white prior. We adopt a mean-field Gaussian posterior with spatially white Gaussian prior:
(r y) = II
N ((()); no(u), diag oz (u)) p(r) = II M(r()): 0, I)
(4)
uE
En
Me (w), log o (u) 9v(v(NN
r(u) (w) + (u) o su)),
s(u) NO I). (5)
This yields an implicit, spatially varying Gaussian family degradations, inferred from local evi- dence via shared, receptive-field-lmmie networks.
3.2 SAVL-LM: CONDITIONAL ELBO
To address the ligeliill term Ellog ( z)] in equation 2 we maximize its evidence lower bound (ELBO). For any variational posterior (r v), Jensen's 's ieequllity yields
(6) derivation of equation 6 is provided in Appendix A.I Under equation 4 the Bellback-Leibllee term has the closed form
log (v > (LM(0, v)) := | log (y 2,r)] Dr v) [I
DKu(gellP) = ΣΣ m
(w) o (u) log o (4) -1).
WER
3.3 SAVL-MIS: MUTUAL-INFORMATTU SUPPRESSION
To address the mutual information term [(r; z) in equation 2 our goal is to derive and minimize tractable upper bound, thereby suppressing the dependence of the degradation variables the content code z. Starting from the identity
T(r; 2) I(r:y) [(2; I(y; +
where C' is a constant, we note that [(2 y) does not involve any learnable parameters and is therefore omitted in optimization. A detailed derivation of equation provided in Appendix(A.2 We control
I(r;2) by (i) an upper bound on I(r: y) using a VIB-style term Alemi et al. (2019) and (ii) lower
bound on T(y; 2, r) via Barber-Agakov term Barber & Agakov (2004).
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Map
Vanance Map
Conv
None Conv
Soare
Severry
Cony
SE11
LaySoom
Scare
F.
Scveniyy
BA-SW-MSA
Degradation Awware SW-MSA
Figure 4: DSCA: Degradation Guideed Spatial and Channel Attention.
Upper and lower bounds. For any y' (r) and go(r) y).
I(r;y) s Eu DKalgalr v) (Ir((r)) + Ci
(9)
and for any critic Po r)r),
2);;,r) 2 Eng(ria) [log
[2,)]] + C2,
(10)
where C1 and C2 are constants independent of the learnable parameters. Combining equa-
tion 8 equation 10 yields the MIS penalty (up additive constant C") ))
I(r;2) Euf (Kaalas(r v) Il [l)((r)) Eao(r Eestrloilloggooe [logpoly 2, 12,r)] C".
(11)
Aligument with SAVL-LM. To obtain estimator- sharing form, we finally unify the three com--
ponents: (i) reuse the likelihood as the critic by setting 0 0, (ii) share the amortized posterior by setting Φ = tb and (iii) adopt the same spatially white prior as in equation by setting y = p(r)- With these identifications, constants are dropped and the composed objective reduces to
JSAVL (0, v- E [log [,) (1 A) Dru(us(r (r y) p(r)))
(12)
where the reconstruction and KL terms are estimated by the same amortized networks and neighbor- hoods as SAVL-LM. In practice, we model the conditional likelihood per pixel as either Gaussian
or Laplace, that the reconstruction term reduces an L2 L1 loss, respectively:
Liee EwER (lb(u) ūs (u; 2, r) (Gaussian)
(13)
Euen (lv(u) 9o Go z, r),,, (Laplace).
In practice we use tunable weights:
min
ftrain a Lree 0b) +3 Pra(ge(r v) l((r))
(14)
8.0
with > 0 and 3 2 O.
3.4 LEVERAGING LEARNED REPRESENTATIONS IN SUPER- RESOLRRNSOOOIOON
Building upon the utility attention in super-resolution Liang et al. (2021a) Zhou et al. (2023);
Chen et al. (2023a), integrate degradation representation, r, via Degradation- Spatial-Channel Attention (DSCA) module (Fig. 4. DSCA leverages the two components of the per-pixel Gaussian posterior for dual-modulation strategy: the posterior variance guides spatial attention, while the posterior mode (mean) guides channel attention.
This is implemented follows. For spatial attention, we define degradation severity as the posterior variance to reweight SW-MSA Liang el al. al scores, promoting attention between pixels with similar degradation. For channel attention, a lightweight convolutional network uses the posterior mode to predict channel-wiss modulation vector, adjusting feature activations based on the inferred degradation type.
EXPERIMENTS
4.1 EXPERIMENT SETTINGS
To validate the two primary properties of our learned degradation representation, we conduct a series of analyses. First, verify that representation effectively captures spatial heterogeneity,

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Method
BSIC SeanlD).
Nels-Leveli
MINE
Nuise-Lodd
Neine-Level
MANe
0.0056
010343
0.1707
6.1286
9,3353
0,3150
DASR
0.0157
<000000]
0.3079
<0.0001
0.6065
0,2000
Ciiforne
00131
0.0270
1,2399
1,2694
0.3456
6.899
LightBRR
0,059
0.0133
0.8223
1.2425
0,4302
0:910
6:3400
SAYE
1,054
Ours vol SAVY C728
010000
<0000001
0.2700
<0.0001
0.3664
0.7009
Table I: Dependency metrics and linear- linear-probe accuracies SVSR Aakerberg al. (2024) with
ablations. Lower association/accuracyy w.r.t. Scene ID and higher association/accurag w.r.t noise- level indicate better degradation representation.
Smooth Texture
Depth increases
iphone (NIQEJ:: 4,7507)
sony (NIQEL: 4.1915)
(a) Cross-Device heterogeneity reflected by our represen- (b) Intra-image degradation heterogeneity re- flected by our representation.
tation.
Figure Visualization of local degradation severity.
we perform a qualitative analysis using the degradation severity map introduced Sec. 4.2 This
map allows for visual inspection of the model's ability identify spatially varying degradation patterns arising from complex factors. Second, to confirm that the representation is decoupled from content, qualitatively assess the structure of the latent space via visualization. Quantitatively, we assess the representation's 's correlation with image content and degradation factors via linear probing Alain & Bengio (2016); [hen al. (2020), and directly test for statistical independence using both the Scchirt-Schmidt Independence Criterion (HSIC) Gretton et al. (2007) and Mutual Information Neural Estimation (MIN))/Belghz)Z al. (2018)
Another aspect that needs to be validated is the performance of our degradation aware super- resolution. We train on the synthetic dataset from Real-ESRGAN Wang et al. (202Tb), which constructed using ground-truth images from DF2K Agustsson & Timofte (2017): Lim et al. (2017)
and OutdoorScene Training Wang et al. (018a)). We evaluate the trained model on both synthetic
and real-world benchmarks Wei et al. (020); Cai al. (2019); Aakerberg al. (2024). During
testing, all images are processed as 256 x 256 tiles, with further details provided Sec. 4.3
We adopt a two-stage schedule in single pipeline: we jointly train SAVL and the SR backbone for 200K iterations to convergence, after which we fine-tune the SR model with an & loss for 800K iterations followed by <==perooptuall+advvrrrrr
losses for an additional 600K iterations; unless otherwise noted, we use Adam (31=0.9, 32-0.99). a batch size of 32, and 64 patches, with an initial learning rate of 2 10 in & phases and 1 10- in adversarial phases, halved every 200K iterations; all experiments are implemented in PyTorch and executed on an 8x NVIDIA RTX 3090 GPU setup. The following standard metrics used for performance comparison: PSNR, SSIM, and LPIPSIZhang et al. (2018)). More implementation details are provided in the Appendix A.4
4.2 EFFECTIVENESS OF DEGRADATION REPRESENTATION
Visualization of Local Degradation Severity. We define the per-pixel degradation severity by single normalized transform of the posterior variance map:
s(u) = 1 (3(2)) Mo3
(15)
Var
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CDFormer
KDSR
Ours
Figure 6: Visualization of the learned representation space. Our representations form well-separated
clusters for distinct degradation factors, revealing strong discrimination of fine-grained variations, whereas other methods tend to collapse these factors into overlapping embeddings. In each grid, the four corners (top-left to bottom-right) correspond to changes in focal length, ISO, sensor type, and texture complexity, respectively. The results of LightBSR under the same setting are summarized in Fig. 1
where (u) denotes the per- per-pixel variance from the mean-field Gaussian posterior in equation 5 and Mog. and Varlog] are the image-wise pair-wise mean and variance of o respectively.
As shown in Fig. 5 the ability to capture local degradation patterns demonstrates the spatial sen- sitivity and interpretability of our learned degradation representation. For cross-device heterogene- ity in Fig. aa.), using same-scene images from different devices in DPED Ignatov et al. (2017), the representation reliably reflects degradation severity: images from the iPhone, which exhibit stronger degradations, yield heatmaps that are more intense and spatially concentrated, consistent with NIQE Mittal et al. (2012) scores. For intra-image heterogeneity in Fig. big. the representation reveals spatial variations in degradation severity as a function of scene depth and texture complexity.
Assessment Across Degradation Factors. We qualitatively assess discriminability by visualizing the learned latent space (PCA to 32D, then t-SNE to 2D) for our method versus haselines (Fig. 6))
To isolate specific degradation factors, we leverage datasets that provide captures of identical scenes while varying a single degradation source: SVSR Aakerberg et al.] (2024) for ISO, RealSR Cal al. (2019) for focal length, and DPED Ignatov et al. (2017) for camera sensors. In each of these fixed-scene settings, as well as for intra-image variations in local texture, the results show a clear distinction between our method and the baseline. While baseline representations fail to distinguish the induced variations and remain entangled, our representation exhibits a clear, degradation-driven structure, forming distinct clusters that align with the specific factor (e.g., noise level, lens setting, or complexity bin).
We then quantify these trends via linear probing and dependence analyses using the Independence Criterion (HSIC) and Mutual Information Neural Estimation (MIN)). We instantiate the degradation factor as ISO-induced noise level and define image content by scene identity (Scene ID). As detailed in Table D the results reveal a sharp contrast in predictability: while baselines like KDSR and CDFormer retain high accuracy for both factors (> 83% for Scene ID), SAVL maintains high degradation accuracy (94.8%% while suppressing content accuracy to chance- level (49.97 %).. This effective decoupling is further validated by dependence metrics, where SAVI. achieves an order-of-magnitue lower MINE score with content (e.g., 0.1507 vs. 1 1.2209 for CD- Former), confirming that our method achieves disentanglement through active information suppres- sion.
4.3 EVALUATION ON REAL- WORLD SUPER-RESOLUTION
We train our model on the synthetic dataset provided by Real-ESRGAN Wang et al. and evaluate on both synthetic and real-world benchmarks, including RealSR Cai et al.
202
2009
DRealSR Wei et al. (2020), SVSR Aakerberg et al. (2024), and RealSRSet25 Zhang et al.
2021
As shown in Table our method consistently outperforms state-of-the-art apppaaaac demonstrat-
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L-
KOSR
Lightssi
1.0
KTSR
CDNteneer
LighTeR
HAY-DAN
Suales
DIBBIR
O2i
HALGAN
Stallen
Okex
Figure 7: Qualitative comparison of SR results on the RealSet25 dataset. Our method yields more faithful texture and structural restoration while avoiding hallucinations and artifacts.
Methods
Panams (M)
GELOPs
BSRGAN
2021
061
19.55
117422
0.2853
0.3950
25.099
0.3321
RoAFSRANN Whny al 5001
16.3
661
19.02
0.5154
9961
24.32
2401
0.2901
26.05
0. 0.7812
0.1876
36.96
0.758
0.3564
SwatR-GANN ling stall
11.8
19.06
2965
0.7680
27.00
0.3788
28
0.743
3.90
20.8
27.76
DASBLJang dal
8007
181
19.75
0.512
0-430
25.51
0. 17525
0. 1301
28.19
01001
0.1165
38.51
0.790
0.9312
DimIR al
NO0th
1670
19.90
0.9987
0.3866
24.98
000000
0.3436
27.16
0.714
0.3900
23M7
6,7019
0.133
BoShil
A021
19.50
0.445
24.77
0.7204
KOSS Ni
15.8
482
0.2007
0.366
0.7M1
0.3436
CDRtemm Lard
25.0
725
19.91
0,5302
0.3839
25.45
0. 077500
(.3641
20.11
0.7792
0,3102
25.07
0.7862
0.3124
Lightiis
Yean
31
205
19.41
0.5175
014650
24.08
0 10065
27.69
0.7718
0.310
Chosser Musistotion
0,4006
Oure mil Speriol Machiettion
136
663
70.01
0.5416
IL3776
25.00
0.7692
0.2687
28.24
0.8132
0.3115
25.09
0.7864
0.316)
Table 2: Evaluation on synthetic and real SR datasets (x4).
ing increasingly significant gains as data complexity rises. While showing steady improvement on synthetic DIV2K (+0.11 dB over RealESRGAN), SAVL achieves on heterogeneous real-world datasets: outperforming KDSR by 0.23 dB on RealSR and surpassing LightBSR by 0.58 dB the highly complex DRealSR. Furthermore, compared diffusion-based methods [in et al. (2024b); Wang et al. (2024), SAVI. achieves superior fidelity (e.g., SSIM 0.8139 0.7830 for St on DR)alSR). Qualitative results in Fig. 7 further underscore this superior- ity, confirming that SAVL faithfully restores texture and structural details while effectively avoiding the hallucinations and semantic artifacts often introduced by generative priors.
pronounced advantage
4.4 ABLATION STUDY
We ablate SAVL with two variants: (i) a deterministic per-pixel degradation code without any mutual-information (MI) constraint, and (ii) the same deterministic code regularized by the CLUB MI upper bound CCeng et al. (2020). On SVSR [Akkerberg et al.) (2024), under the protocol of Sec. 4.2 we report quantitative metrics in Table and qualitative t-SNE visualizations in Fig. g Un- der fixed-scene comparisons, either ablation collapses separability across ISO (noise-level) strata. We further ablate the degradation-an -arrae SR by removing channel or spatial feature modulations; results in Table 2 are obtained under identical training and testing protocols to isolate each compo- nent's contribution.
5 CONCLUSION
We introduced SAVL., a spatially amortized variational framework that learns a per-pixel, spatially varying degradation representation. By enforcing information-theoreti constraint, SAVL disen- tangles this representation from image content, making highly discriminative of diverse degrada- tion factors. We leverage this representation through a novel dual-guidance mechanism that uses the posterior's 's mode for channel modulation and its variance for spatial modulation; this seamless integration into SR networks yields consistent performance gains both synthetic and real-world datasets. Extensive qualitative and quantitative analyses, corroborated by ablation studies, con- firm that representation captures spatial heterogeneity while remaining strongly correlated with

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Dogg alation Reprorntation azos I5
150309 Degrabation Reprosettatoo arooa 150s
Dogadaaio Seporrtatoo aore 1uh
100
(a) Full model
(b) w/o SAVL
(c) w/ SAVL + CLUB
Figure Qualitative effects of ablations (PCA++-SNE).
degradation and minimally with content, and verify the essential contributions of our proposed com- ponents.
ACKNOWLEDGMENTS
This work is supported in part by National Natural Science Foundation of China (NSFC) under Grant No.62376292, Guangdong Provincial General Fund No. 2024A1515010208, Guangzhou Science and Technology Program Project No.220254445466. Guangdong Basic and Applied Basic Research Foundation Under Grant No. 2024A1515011741.
REFERENCES
Andreas Aakerberg, Majed El Helou, Kamal Nasrollahi, and Thomas Moeslund. Pda-rwsr: Pixel- wise degradation adaptive real-world super-resolution. In Proceedings of the IEEF/CVF Winter Conference on Applications of Computer Vision, pp. 4097-4107, 2024.
Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. In The IEEE Conference Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprini 1rXiv:1610.01644, 2016.
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy, Deep variational information bottleneck, 2019. URL
wttp://...ggab////lle/.... 00410
David Barber and Felix Agakov. The im algorithm: variational approach to information maxi- mization. Advances in neural information processing systems, 16(320):201, 2004.
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. Mutual information neural estimation. International conference machine learning, pp. 531-540. PMLR, 2018
Jianrui Cai, Hui Zeng, Hongwei Yong. Zisheng Cao, and Lei Zhang. Toward real-world single image super-resolution: A new benchmark and a new model. In Proceedings of the IEEE/CVF international conference computer vision, pp. 3086-3095, 2019.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning visual representations. International conference on machine learning, pp. 1597-1607. PmLR, 2020.
Xiangyu Chen, Xintao Wang, Wenlong Zhang, Xiangtao Kong, Yu Qiao, Jiantao Zhou, and Chao Dong. Hat: Hybrid attention transformer for image restoration. arXiv preprint arXiv:2309.05239, 2023a.
10

---

Published as a conference paper at ICLR 2026
Xuhai Chen, Jiangning Zhang, Chao Xu, Yabiao Wang, Chengjie Wang, and Yong Liu. Better" cmos" produces clearer images: Learning space-variant blur estimation for blind image super- resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- fion, pp. 1651-1661, 2023b.
Pengyu Cheng, Weituo Hao, Shuyang Dai. Jiachang Liu. Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound mutual information. In International conference on machine learning, pp. 1779-1788. PMLR, 2020.
Chao Dong, Chen Change Loy. Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2): 295-307, 2015.
Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song. Bernhard Schölkopf, and Alex Smola. A kernel statistical test of independence. Advances in neurul information processing systems, 20, 2007.
Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong. Blind super-resolution with iterative kernel correction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- tion, pp. 1604-1613, 2019.
Shaojie Guo, Haofei Song, Qingli Li, and Yan Wang. Spatially-variant degradation model for dataset-free super-resolution. In European Conference on Computer Vision, pp. 340-357. Springer, 2024a.
Xingbei Guo, Ziping Ma. Qing Wang, and Pengxu Wei. Towards real-world continuous super- resolution: Benchmark and method. In 2024 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6. IEEE, 2024b.
Yan Huang, Shang Li, Liang Wang, Tieniu Tan, et al. Unfolding the alternating optimization for blind super resolution. Advances in Neural Information Processing Systems, 33:5632-5643, 2020.
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslr- quality photos on mobile devices with deep convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, pp. 3277-3285, 2017.
Soo Ye Kim, Hyeonjun Sim, and Munchurl Kim. Koalanet: Blind super-resolution using kemel- oriented adaptive local adjustment. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 1061 1-06200, 2021.
Christian Ledig, Lucas Theis, Ferenc Huszár. Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic sin- gle image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 16844611444 1691-4690, 2017.
Shang Li, Guixuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, and Shuwu Zhang. Degradation regression with uncertainty for blind super-resolution. Neurocomputing, 582:127486, 2024.
Jie Liang Hui Zeng. and Lei Zhang. Efficient and degradation-adaativ network for real-world image super-resolution In European Conference on Computer Vision, pp. 574-591, Springer, 2022.
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Lue Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pp. 1833-1844, 2021a.
Jingyun Liang, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Mutual affine network for spatially variant kernel estimation in blind image super-resolution. In Proceedings of the IEFE/CVF International Conference on Computer Vision, pp. 4096-4105, 2021b.
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep resid- ual networks for single image super- resoutinsol In Proceedings of the IEEE conference on com- puler vision and pattern recognition workshops, pp. 136-144, 2017.
11

---

Published as a conference paper at ICLR 2026
Junxiong Lin, Zen Tao, Xuan Tong, Xinji Mai, Haoran Wang, Boyang Wang, Yan Wang, Qing Zhao, Jiawen Yu, Yuxuan Lin, et al. Suppressing uncertainties in degradation estimation for blind super-resolution. In Proceedings of the 32nd ACM International Conference Multimedia, pp- 6374-6383, 2024a.
Xinqi Lin, Jingwen He. Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Yu Qiao, Wanli Ouyang, and Chao Dong, Diffbir: Toward blind image restoration with generative diffusion prior. In European Conference on Computer Vision, pp. 430 448. Springer, 2024b.
Qingguo Liu, Chenyi Zhuang, Pan Gao, and Jie Qin. Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7455-7464 2024.
David Martin, Charless Fowlkes Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings eighth IEEE international conference on computer vision. ICCV 2001, volume 2. pp. 416 423. IEEE, 2001.
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making "completely blind" image quality analyzer. IEEE Signal processing letters, 20(3):20--22,, 2012.
Chong Mou, Yanze Wu, Xintao Wang. Chao Dong, Jian Zhang, and Ying Shan. Metric learning based interactive modulation for real-world super-resolution. In European Conference on Com- puler Vision, pp. 723-740. Springer, 2022.
Jianyi Wang, Zongsheng Yue, Shangehen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5929-594 2024.
Longguang Wang. Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang. Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind super-resolution. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10581-10590, 2021a.
Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 606-615, 2018 8a.
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong. Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, pp. 0-0, 2018b.
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pp. 1905-1914, 2021b.
Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, Wangmeng Zuo, and Liang Lin. Component divide-and-conquee for real-world image super-resolution. In Computer Vision- ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VIII 16. pp. 101-117. Springer, 2020.
Pengxu Wei, Yujing Sun, Xingbei Guo, Chang Liu, Guanbin Li, Jie Chen, Xiangyang Ji, and Liang Lin. Towards real-world burst image super-resolution: Benchmark and method. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13233-13242, 2023a.
Pengxu Wei, Ziwei Xie, Guanbin Li, and Liang Lin. Taylor neural network for real-world image super-resolution. IEEE Trunsuctions on Image Processing, 32: 1944-1999 2023b.
Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang. Radu Timofte, and Luc Van Gool. Knowledge distillation based degradation estimation for blind super-resolution. arXiv preprint arXiv:2211. 1..::2//1..192.... 2022.
12

---

Published as a conference paper at ICLR 2026
Bin Xia, Yapeng Tian, Yulun Zhang, Yucheng Hang, Wenming Yang, and Qingmin Liao. Meta- searning-based degradation representation for blind super-resolution, IEEE transactions on image processing. 32:3383-3390 2023.
Xiaoqian Xu, Pengxu Wei, Weikai Chen, Yang Liu, Mingzhi Mao. Liang Lin, and Guanbin Li. Dual adversarial adaptation for cross-device real-world image super-resolution. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 5667-5676, 2022.
Yu-Syuan Xu, Shou- Yao Roy Tseng. Yu Tseng, Hsien- Kai Kuo, and Yi-Min Tsai. Unified dynamic convolutional network for super-resolution with variational degradations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12496-12505, 2020.
Jiang Yuan, Ji Ma. Bo Wang, and Weiming Hu. Content-decoupled contrastive learning-based im- plicit degradation modeling for blind image super-resolution. arXiv preprint arXiv:2408. 05440, 2024.
Jiang Yuan, JI Ma, Bo Wang. Guanzhou Ke, and Weiming Hu. Lightbsr: Towards lightweight blind super-resolution via discriminative implicit degradation representation learning. arXivv preprint arXiv:2506.22710, 2025.
Zongsheng Yue, Jianyi Wang, and Chen Change Loy. Resshift: Efficient diffusion model for image super-resolution by residual shifting. Advances in Neural Information Processing Systems, 36: 13294-13307, 2023.
Kai Zhang, Wangmeng Zuo, and Lei Zhang. Learning a single convolutional super-resolution net- work for multiple degradations. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 3262-3271, 2018a.
Kai Zhang, Wangmeng Zuo, and Lei Zhang. Deep plug-and and-play super-resolution for arbitrary blur kernels. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pp. 1671-1681, 2019.
Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte. Designing practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4791 1791- 2021.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In CVPR, 2018b.
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super- resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), pp. 286-301, 2018, 8c.
Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, and Qibin Hou. Srformer: Permuted self-attention for single image super-resolution. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision pp. 12780-12791, 2023.
A APPENDIX
A.I
DERIVATION OF THE CONDITIONAL ELBO
log Po 2) = log / po(y r.,.rl 22) dr (def.)
(16)
log / p(r) poly 12,)) dr (factorize pe pp.rr (2) = p(r) pa(y) 2,r))
(17)
log / (asrr vv) (v 2, p(r) dr (x ge go (r (v)
(18)
ge
Po p(r)
log Bgg(ria))
(19)
Lo y)
Byg (riw) log log [,r]]] Dralav(1) v) lI((r))) (Jensen)
(20)
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Method
Params
PSNR
SSIM 1
LPIPS
CDC
Wei al.
2020
40.3M
32.08
0.8585
0.3095
SwinIR
Liong ct al
2021a
118M
31.74
0.8466
0.3317
HAT
Chen al.
2023a
20.8M
31.93
0.8506
0.3197
et
31.97
0.8533
0.3116
CDFormer
2024
31.77
0.8459
0.3320
ResShift Yuc
2023
118.6M
29.56
0,7812
0.3932
Ours
14.0M
32.27
0.8608
0.2994
Table 3: Results on DRealSR test set (x4).
A.2 DERIVATION OF THE MUTUAL-INFORMATION IDENTITY
21,r1;)) 1(r;2) I(r; v) = I(r:y) - [(r;y) 2) = I(2;)) - I(2; (r)
(interaction info)
(21)
T(y;,,r) T(y;=) T(u:r 2) T(y;r) T(y; 1r)
(chain rule)
(22)
I(r;z) I(r;g) + [2;y) 1yy; r) + I(r; 1 v)
(rearrange)
(23)
Under (r: 2. 3) Paata (I, v) (r v) with = ((x),
p(r, y) = (r(r y) (= y) = r 11 2 = T(r; (r)= =0 (CI)
(24)
Hence
2();2) [(r: 3) + I [2; (1, (Here = (xx) is deterministic with fixed D, so (z; is constant w.r.t. learnable parameters.)
(25)
(26)
A.3 ADDITIONAL EXPERIMENTAL RESULTS
We train our method on the large-scale paired real-world dataset DRealSR Wei et al. (2020). We
evaluate our model DRealSR test set under scale of x4. All comparison models retrained the DRealSR training set with the same setting. The results are summarized in Table. 3 Our method outperforms state-of-the-arr real-world SR methods and degradation representation approaches.
To further validate the discriminability of our degradation representation in modeling spatially vari- roal-world degradations, additionally conduct super-resolution experiments synthetic spa- tially variant blur kernels. On the BSD ASDIOonMartin Martin et al. (2001) dataset, each image is degraded with spatially varying kernels, where different regions (patches) are subjected different blur patterns according to fixed degradation protocol proposed in MANet Liang et al. (2021b). As shown in Table. 4 although our representation does not utilize any GT kernel supervision, achieves compa- rable or even superior guidance performance to supervised predicted kernels.
A.4 ADDITIONAL EXPERIMENTAL DETAILS
Training objective. optimize a pixelwise reconstruction loss together with a KL regularizer in SAVL, in line with the composed objective:
(s, v/) = - (riv) log po(y) [2,]]] (1 + DKI(gw vv) P()))
(27)
where the reconstruction and KI. terms estimated by the amortized networks and neigh- borhoods as in SAVL.-LM. In practice we model the per-pixel conditional likelihood as Laplace so the reconstruction reduces an loss:
Liee
Σ [b(()) - Do (u; 2, Il
(28)
REG
We train with tunable weights and annealing to prevent collapse:
min Lerain Lree + Br (r w) l))(r)). 6,0
(29)
3: = Baual min (1, , Ahnal
5 x 10-4, Tanneal 55,000 iterations,
= 1.
(30)
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Spatially Vaius Kamel Type
Method
og 4-
my
con 8-m
+
-1
my b6l + d M10.m)
Degradation Learning
RRDB Wang ctal
20180
CDP Liu et
2004
24,40 013911
24.71 0,0003
24.78/06130
24.99 0.6170
24.91
GT-Kernd Supervisc Methons
24.44 005900
24.94/
2481/06675
25.01
34.05/06608
RRDB-SPI MANr
2001h
24,89
25 0.6192
28/1//0197
0.6200
05/60161
Ours (Ne Kernll Supervision)
SPT
24.55 (06015
0.620
25.18/ (L6199
25,22/
25.13/0.612
Upper
RRDB-SF Wary ctal
20186 GT
24 88 6082
25. 52 0.6255
35 35 (0,0002
25. 54/ 0.6344
25 80 (18233
Table 4: Quantitative comparison BSD100 dataset for spatially variant blur kernel degradation at scale x4 and noise level 15. All results are reported in terms of PSNR/SSIM, evaluated on the channel.
Model overview. The network consists of a MANet encoder and decoder that acts posterior estimator. The encoder takes the image y and predicts a per-pixel diagonal Gaussian over degradation code the decoder conditions on and the hr image to produce
Encoder. We use MANet Liang et al. (0221b) the backbone (Conv head + MAConv- residual blocks with shared local receptive fields + Conv tail), with GroupNorm (32 groups) and SiLU/Swish activations. The degradation representation dimension is 64, so the encoder outputs 64 channels (mean and log-variance) for a diagonal Gaussian (r I-) [Nu, diu,diag a²), and sampling uses the standard reparameterization
Decoder (posterior estimator). The decoder fuses the degradation code with image-conditioned features reconstruct ÿ. A single fixed Gaussian blur (kernel 9x9, 0=9.0) forms a low- cow-frequency base and complementary figh-frequency condition; features are fused via an SFT-style block and processed by an RRDB Wang et al. (2018b) trunk (default 8 blocks, growth channels 32, residual scale 0.2), followed by 3-channel prediction head.
Key hyperparameters. Unless otherwise specified: base channels 32; channel multipliers [1, 1, 2, 4]: encoder code dimension 64; decoder encoding scale 0.25; GroupNorm(32); truncated normal weight init.
A.5 FURTHER QUALITATIVE AND QUANTITATIVE EVIDENCE FOR SPATIAL MODELING AND DEGRADATION IIAADATTIO-CONTENT DECOUPLING
In this section, we provide additional experimental evidence, both qualitative and quantitative, demonstrating that SAVL (1) effectively models spatially varying degradations and (2) successfully decouples degradation from content. This section includes the t-SNE analysis, the unsupervised classification study, and more ablation experiments.
A.5.1 MODELING THE SPATIALLY VARIANT DEGRADATIONS
To further illustrate the spatially variant property in a more controlled and quantitative manner, we divide a high-resolution image from DRealSR into patches and randomly apply different degrada- tions to different patches. We then extract pixel-level feature representations and assign each pixel a label corresponding to its degradation type. Using t-SNE, we visualize these representations and perform unsupervised k-means clustering to assess how well the pixel degradations can be sepa- rated. strong representation should exhibit clear clustering patterns and achieve high clustering accuracy.
As shown Fig. we adopt three different strategies to synthesize spatially varying degradation types.
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(a) Spatially variant degradation type.
(b) Spatially variant blur ø.
(c) Spatially variant noise std.
Figure 9: Different spatially varying degradation strategies and their degradation map.
Spatially Variant Degradation
Accuracy
Category
Setting
Ours
MANet
Ours w/o SAVL
Ours w/o SAVL CLUB
Variant Type
Noise / Blur / JPEG
72.44%
43.66%
48.02%
41.68%
Variant Blur Level
0.1 1./////////////
61.20%
37.32%
39.34%
32.48%
Variant Noise Strength
57.60%
37.78%
51.36%
37.64%
Table 5: Clustering accuracy under different spatially varying degradation settings, Our method achieves higher clustering accuracy under 3 spatially variant degradation settings, indicating stronger ability to distinguish spatially varying degradations.
1. Each patch is randomly assigned one of three degradations: noise, blur, or JPEG compres- sion.
2. Each patch is randomly assigned a blur kernel with a different a value.
3. Each patch is randomly assigned Gaussian noise with different standard deviation.
Our method is compared against MANet Liang et al. (2021b), as other approaches model only global degradation and lack pixel-level representation capability. We also include ablation variants for completeness. The results are presented in Fig 10 and Table 5
We then provide more visual evidence demonstrating SAVL's effectiveness in modeling spatially varying degradations. Because the deterministic variant lacks learnable variance, statistically examine the local variance of both deterministic representations as well as the predicted mode of our per-pixel representation. As shown in Fig [1. the deterministic representations exhibit limited spatial variability their spatial variance is relatively uniform and influenced primarily by image brightness (RGB values) rather than by actual degradation factors.
A.5.2 MITIGATING THE DEGRADATION CONTENT ENTANGLEMENT.
Compared to spatial variability, the model's ability to decouple degradation from content is more challenging to visualize directly. Beyond the t-SNE plot presented the main text, we further include visualization based on proxy experiment. In this experiment, extract the content fea- tures of clean image (image A) and the degradation representation a degraded image (image B), and feed them jointly into the decoder. If degradation and content truly decoupled, the decoded result should preserve the content image A while exhibiting the degradation characteristics of image B. Crucially, the hybrid image should contain content or texture originating from image B. As shown in Fig I2 removing SAVL causes the decoded output to be dominated entirely by the content of the degraded image B; the CLUB constraint alleviates this issue some extent, but still introduces noticeable edges and textures from image B. Only with SAVL do we obtain a clean hybrid result that matches the intended combination of "content A + degradation B." This demon- strates that our degradation embedding is a purely "degradation" representation decoupled from the content.
A.6 FURTHER ANALYSIS OF THE POSTERIOR MEAN (THE "MODE")
In the main text, describe the posterior mean of representation encoding the type of degradation. Here, we provide additional experimental analysis to further clarify this interpretation. By fixing all but one degradation factor and varying the remaining one, examine how individual
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pesked lond Oegraaae futurs
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Tea: kedds Claskringg Rec 23.34%
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Figure 10: The t-SNE visualization and clustering accuracy of the pixel-level representations show that our features naturally cluster according to the same degradation type or parameter. Moreover, method achieves higher clustering accuracy, indicating stronger ability distinguish spatially varying degradations.
Input Image
Full model
w/o SAVL
w/o SAVL CLUB
Figure II: The deterministic representations exhibit limited spatial variability-then spatial vari- ance is relatively uniform and influenced primarily by image brightness (RGB values) rather than by actual degradation factors.
dimensions of the mean vector correspond to specific degradation changes. We observe consistent and predictable variations along certain dimensions of the mean vector as the associated degradation factor is altered.
Concretely, we conduct this experiment by fixing two degradation parameters (c.g., noise and JPEG) while systematically varying the third (e.g., blur sigma from 0.2 to 4.0). At each step, extract the
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Deg, Enc
Image B (Degraded)
Decoder
SAVL
w/o SAVL
w/o SAVL CLUB
Cont. Ene
Image (Clean)
Removing SAVL makes the decoded output dominated by the content the degraded image B, while the CLUB constraint only partially improves the result and still introduces edges and textures from image B. Only SAVL produces a clean hybrid image reflecting "content A + degra- dation B," demonstrating that our degradation embedding captures degradation alone and remains decoupled from content.
Figure 12:
Try Smeaite Binmalin back liuras (CAP)
(a) When particular degradation factor changes, certain dimensions of the (b) Subspaces of different vector exhibit monotonic predictable trajectories.
degradation factors
The posterior mean changes smoothly and predictably along specific dimensions single degradation factor varies, illustrating a stable and coherent mapping between degradation strength and the corresponding mean-vector dimensions.
Figure 13:
64-D posterior mean vector (via global average pooling). We identify the top ten dimensions with the largest variance under each perturbation and treat these as the subspace most representative of that particular degradation.
As shown in Fig. 13 our analysis reveals key findings:
(1)Consistent change. The dimensions with the highest variance exhibit smooth, monotonic mapping.
trends as the degradation strength increases, indicating a predictable and
(2) Disentanglement. The dimensions most sensitive to blur tend to differ from those responsive to noise or JPEG, suggesting that the encoder organizes different degradation factors into partially distinct subspaces.
A.7 FURTHER ANALYSIS ON OOD DEGRADATIONS
To investigate the model's behavior entirely out-of-distribution degradation types that are absent during training (e.g., motion blur, atmospheric haze, or sensor-specific noise patterns), we synthesize motion blur with varying kernel lengths and haze with different concentrations. These settings allow us to assess the robustness of our learned degradation representation under truly novel degradation conditions.
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Do gradd an Bapomare acnes Vedian like
Degadadar Repwor May Whater
Depodadio llpporrddon Whiber
CDForner
KDSR
CDFormer
KDSR
Depalete Rorme aemurro Motte
Drgnelete Expemoe Medee
Degodadion Sepe thep Wester
Degrataton
LightBSR
Ours
LightBSR
Ours
(a) Degradation Representations across motion blur.
(b) Degradation Representations across fog.
Figure 14: Visualization of the learned representation space.
Our representation also exhibits
stronger discriminability the OOD data.
Sensor-specific noise patterns have already been examined in the main text through the SVSR dataset Mateset/Aakerberg et al. (2024), which contains noise characteristics unique to specific camera sensors, and our training set does not include any SVSR data. Please refer to fig
We further t-SNE analyze the distribution of the degradation representations in the feature space. As shown in Fig. [4, the probabilistic nature of SAVL provides enhanced robustness to domain shifts compared deterministic methods.
A.8 STABILITY, CONVERGENCE, AND TRAINING COST
Our training objective consists primarily of the KL term and the reconstruction loss. To prevent posterior collapse, we apply KL annealing, gradually increasing the KL weight from to 5e-4 over the first 50k iterations. As shown in Fig. [5] the TensorBoard curves indicate stable training dynam- ies: the KL loss and reconstruction loss counterbalance each other and remain stable once annealing completes, while the reconstruction loss steadily decreases and converges at around 200k iterations. These observations suggest that the mutual-informale timization, and the model's capacity encode degradations improves consistently throughout train- ing. Our approach exhibits strong training stability and reliable convergence throughout the learning process.
constraints remain well-behaved during op-
Our training time is approximately 48 hours for learning the degradation representation and another 48 hours for training the super-resolution network. This comparable multi-stage implicit degra- dation-representn representation methods such as CDFormer Liu et al. 2024) and KDSR Xia et al. (2022), which also require two-stage training involving separate teacher and student models. Our train- ing cost is somewhat higher than lighter-weight implicit approaches like DASR and LightBSR, but remains within similar overall scale.
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Published as a conference paper at ICLR 2026
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Figure 15: TensorBoard loss curves of our training process.
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Reviews:
[{"review_id": "DCR3nhMRFe", "reviewer": "Reviewer_44sz", "summary": "This paper aims to tackle two crucial issues in Single-Image Super-Resolution: inability to model spatially variant degradation and difficulty of degradation-content decoupling. To this end, it proposes Spatially Amortized Variational Learning (SAVL) algorithm, which models per-pixel degradations as spatially varying Gaussians inferred from local neighborhoods. Meanwhile, SAVL imposes an explicit mutual information constraint to suppress the degradation-content entanglement.", "questions": "Check the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper is motivated well and modeling of spatially variant degradation is indeed crucial for single image super resolution. \n2. The proposed Spatially Amortized Variational Learning (SAVL)  framework models per-pixel degradations as spatially varying Gaussians, which is a novel design.", "weaknesses": "1. More visual comparisons for ablation study are favorable to show the effectiveness of the proposed SAVL in 1) modeling the spatially variant degradations and 2) mitigating the degradation-content entanglement.\n\n2. How to perform quantitative evaluation on the effectiveness of SAVL on degradation-content decoupling? It is indeed not straightforward to conduct such evaluation whilst it is important.\n\nMinor issues:\nFigure 1 and Figure 5 are too small to deliver a good presentation.", "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": "4MGaxIw1Y1", "reviewer": "Reviewer_7HwT", "summary": "This paper addresses the challenge of modeling complex, spatially-variant degradations and the common issue of \"degradation-content entanglement\" in Real-World Super-Resolution (RWSR). The proposed Spatially Amortized Variational Learning (SAVL) framework models per-pixel degradation as a spatially varying Gaussian distribution. SAVL uniquely couples two components: A conditional likelihood lane (SAVL-LM) learns the degradation representation by maximizing the Evidence Lower Bound (ELBO). A mutual information suppression lane (SAVL-MIS) explicitly disentangles this representation from image content. Building on this disentangled representation, the learned Gaussian's mode (mean) and variance are leveraged to guide a degradation-aware SR network through channel-wise and spatial attention modulation, respectively. Extensive experiments demonstrate that this approach successfully captures the spatial distribution of complex degradations, leading to consistent, state-of-the-art performance on real-world datasets.", "questions": "1. Have the authors conducted any experiments to probe the learned 64-D mean space? For example, by systematically varying a single degradation parameter (e.g., the kernel size of a Gaussian blur) while keeping others constant, is it possible to observe a consistent, predictable change along certain dimensions of the mean vector?\n2. The final objective (Eq. 12) simplifies the mutual information (MI) upper bound into a KL divergence term by aligning the priors. Did the authors experiment with using other direct MI estimators, such as CLUB (which was used in the ablation study), as a regularizer in the main objective instead of the VIB-style upper bound? How would integrating such an estimator directly into the SAVL framework compare to the current formulation?\n3. How does the model perform on entirely out-of-distribution degradation types not seen during training (e.g., motion blur, atmospheric haze, or specific sensor noise patterns)? Does the probabilistic nature of SAVL provide enhanced robustness to such domain shifts compared to deterministic methods?\n4. In Section 3.4, it is stated that the variance map guides spatial attention by re-weighting the scores in SW-MSA. Could the authors provide more specific details on this operation? Is it a simple element-wise multiplication of the attention scores with the (processed) variance map, or is a more complex fusion mechanism employed?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper identifies degradation–content entanglement as RWSR’s key bottleneck, especially under spatially varying degradations, and addresses it with an optimization objective under an information-theoretic constraint.\n2. The SAVL framework offers a principled, theoretically-grounded solution for disentanglement. By modeling degradation as a probability distribution, it naturally captures uncertainty and \"severity\" (via the variance) and cleverly uses the posterior's mode and variance in a dual-guidance mechanism for channel and spatial modulation.\n3. Extensive experiments demonstrate that the proposed approach accurately captures the spatial distribution of complex degradations and achieves consistent state-of-the-art performance on real-world datasets.\n4. This paper is well-written and well-structured, with clear motivation.", "weaknesses": "1. While the posterior variance is intuitively interpreted as degradation \"severity,\" the meaning of the posterior mean (the \"mode\") remains less explored. The paper suggests it \"characterizes the degradation type,\" but the 64-dimensional latent space is treated as a black box. A deeper analysis, for instance, investigating whether specific dimensions or subspaces correlate with distinct degradation factors (e.g., blur, noise, compression), would further strengthen the paper's claims and provide valuable insights into the learned representation.\n2. The SAVL framework is trained on paired data, which in practice is generated by applying a synthetic degradation pipeline (from Real-ESRGAN) to high-quality images. While this is a standard practice, the generalization capability of the learned representation is ultimately bounded by the diversity and fidelity of this synthetic pipeline. Generalization may be limited when real-world degradations are mismatched with or underrepresented in the synthetic training set.\n3. Although the GFLOPs reported in Table 2 are competitive, the SAVL framework introduces additional complexity through variational inference and mutual information regularization. A brief discussion on training stability, convergence speed, and the overall training time/cost compared to simpler implicit representation learning methods would be beneficial.", "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": "NY2TrR8aV6", "reviewer": "Reviewer_ZWQc", "summary": "This paper introduces a novel framework called Spatially Amortized Variational Learning (SAVL) to address some challenges of Real-World Super-Resolution (RWSR), which are spatial variance and entanglement between degradation and image content. SAVL models per-pixel degradations as spatially varying Gaussian distributions inferred from local neighborhoods. It combines a conditional likelihood module (SAVL-LM) with a mutual information suppression module (SAVL-MIS) to disentangle degradation from content. This yields a well-constrained latent space that effectively captures degradation heterogeneity. The learned representations are then integrated into a degradation-aware super-resolution network using channel-wise and spatial attention modulation.", "questions": "1. There are many latex encoding errors in Figure 2, displaying as a \"?\" with bounding box below the text \"Amortized Estimator\", after the text \"Per-pixel Gaussian sample\", and above several arrors. I don't understand the original notation at these places.\n2. In Section 3.4, the paper claims \"the posterior variance guides spatial attention,while the posterior mode (mean) guides channel attention\". Please provide more explanation and analysis on this design.\n3. The proposed SAVL aims to solve both spatial variance of degradation and entanglement between degradation and image content, but I only see experimental verification of the later but no justification for the former. Do you provide any numerial or visual evidence to verify that your proposed spatially varying Gaussian modeling solves this chanllenge?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. SAVL introduces a per-pixel Gaussian representation that captures spatially varying degradations across an image. This allows the model to handle real-world scenarios where degradation is non-uniform and locally complex, outperforming traditional global or patch-based approaches.\n2. By incorporating a mutual information suppression term into its variational objective, SAVL explicitly filters out content-related signals from the degradation representation. This results in a latent space that is highly discriminative of degradation factors while minimizing entanglement with image semantics.\n3. The presentation of the ideas is relatively clear and easy to follow. The illustraions are good, especially figure 1, though there are some formats issues.", "weaknesses": "1. There exists severe format issues in Figure 2, which is very improfessional and impacts reviews' understand of the content. For the details, please see the question part. \n2. The paper claims it outperforms state-of-the-art methods, but the most recent SOTA approach in the comparison was published on CVPR 2024, which was 2 years ago. I suggest it should include more updated works, perhaps published on CVPR 2025 and ICCV 2025, in both quantitative and qualitative comparisons.\n3. Qualitative comparisons of SR results (Fig. 6 and 8) are all conducted on old methods, no visual comparisons with more up-to-date methods such as DiffBIR, StableSR, and CDFormer.", "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": "Okj1MrefQX", "reviewer": "Reviewer_kNqe", "summary": "The paper suggests framework for learning degradation representation which effectively captures the spatial distribution of complex degradations and also can be decoupled with contents of the image. Using the proposed degradation representation, paper outperforms the previous baseline with clear margin.", "questions": "Refer to the weakness section.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper suggests the good motivation. Previous degradation representation learning methods didn't consider that 1) degradations are not uniformly distributed within an image and 2) the degradation should be decoupled with the content. These two limitations are clearly shown in the Fig 1. and the experiments.\n\n* Paper achieves fair contribution to mitigate aforementioned issues with mathematical demonstration.\n\n* Experiments and ablation study is thorough, demonstrating the effectiveness of paper's framework.", "weaknesses": "* In Tab. 1, what does 'CLUB' mean here? It is not mentioned in the previous section. Which part of the method section is related with this?\n* The explanation about the experiment results is too brief. There is only one sentence for each table from Tab.1 to Tab. 2 which is like \"the proposed method outperforms the previous methods\".\n* In the right hand side of the image, I can't find clear improvement of the paper compared to the baseline.", "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": "dw0LrpHkLj", "reviewer": "Reviewer_XJzK", "summary": "This paper works on real-world super-resolution and proposes Spatially Amortized Variational Learning (SAVL), which learns pixel-wise degradation representations through spatially varying Gaussian posteriors inferred from local neighborhoods. SAVL integrates two components: SAVL-LM, for conditional likelihood learning via an amortized variational bound, and SAVL-MIS, which suppresses mutual information to disentangle degradation from content. The learned representations guide the downstream SR network via spatial and channel modulation. Experiments on both synthetic and real datasets (e.g., RealSR, DRealSR, SVSR) show moderate improvements over prior methods such as LightBSR and CDFormer.", "questions": "1. It is better to provide more evidence on synthetic spatially varying degradations (e.g., blur kernels, noise maps) with quantitative evaluation of SAVL’s per-pixel degradation accuracy.\n2. Include additional visual comparisons with recent diffusion-based SR methods to better assess SAVL’s performance against current state-of-the-art approaches.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper explores the relatively underexplored direction of modeling spatially heterogeneous real-world degradations through pixel-wise variational distributions. Using degradation learning as an amortized variational inference problem and integrating information-theoretic regularization could inspire follow-up work.\n2. The proposed SAVL framework and its joint training strategy are well-formulated and explained in detail.\n3. The authors provide experiments and visual analyses to demonstrate both the quality of the learned degradation representations and their impact on super-resolution performance.", "weaknesses": "1. The comparisons rely mostly on older baselines (e.g., HAT, StableSR, CDFormer) and do not include recent stronger diffusion-based super-resolution models. It therefore remains unclear whether the proposed method truly advances the state of real-world super-resolution.\n2. While qualitative heatmaps and t-SNE plots are shown, quantitative validation of spatial degradation modeling is limited. The experiments appear to rely on simplified or toy setups, and the actual benefit of the per-pixel Gaussian modeling requires more investigation.\n3. The evaluation primarily uses reference-based metrics (PSNR, SSIM, LPIPS), without sufficient inclusion of no-reference metrics (e.g., MANIQA, MUSIQ, CLIP-IQA) that are more appropriate for real-world settings.\n4. The paper’s presentation quality could be improved. For instance, improving figure readability (fonts, labels) and providing clearer, more descriptive captions.", "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)