Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
Authors: Yidi Liu
Xueyang Fu
Jie Huang
Jie Xiao
Dong Li
Wenlong Zhang
LEI BAI
Zheng-Jun Zha
Venue: NeurIPS 2025 poster ()
Keywords: Ultra-high-definition
Latent Regularization
Image Restoration
Fidelity-Perception Trade-off
Primary area: deep_learning
TL;DR: This paper presents the "Latent Harmony" framework, which achieves synergistic unified Ultra-High Definition (UHD) image restoration through latent space regularization and controllable refinement.

Abstract: Ultra-High Definition (UHD) image restoration struggles to balance computational efficiency and detail retention.
While Variational Autoencoders (VAEs) offer improved efficiency by operating in the latent space, with the Gaussian variational constraint, this compression preserves semantics but sacrifices critical high-frequency attributes specific to degradation and thus compromises reconstruction fidelity. 
% This compromises reconstruction fidelity, even when global semantics are preserved.
Consequently, a VAE redesign is imperative to foster a robust semantic representation conducive to generalization and perceptual quality, while simultaneously enabling effective high-frequency information processing crucial for reconstruction fidelity.
To address this, we propose  \textit{Latent Harmony}, a two-stage framework that reinvigorates VAEs for UHD restoration by concurrently regularizing the latent space and enforcing high-frequency-aware reconstruction constraints. 
Specifically, Stage One introduces the LH-VAE, which fortifies its latent representation through visual semantic constraints and progressive degradation perturbation for enhanced semantics robustness; meanwhile, it incorporates latent equivariance to bolster its high-frequency reconstruction capabilities. 
Then, Stage Two facilitates joint training of this refined VAE with a dedicated restoration model.
This stage integrates High-Frequency Low-Rank Adaptation (HF-LoRA), featuring two distinct modules: an encoder LoRA, guided by a fidelity-oriented high-frequency alignment loss, tailored for the precise extraction of authentic details from degradation-sensitive high-frequency components; and a decoder LoRA, driven by a perception-oriented loss, designed to synthesize perceptually superior textures. These LoRA modules are meticulously trained via alternating optimization with selective gradient propagation to preserve the integrity of the pre-trained latent structure. This methodology culminates in a flexible fidelity-perception trade-off at inference, managed by an adjustable parameter 
$\alpha$.
Extensive experiments demonstrate that \textit{Latent Harmony} effectively balances perceptual and reconstructive objectives with efficiency, achieving superior restoration performance across diverse UHD and standard-resolution  scenarios.

Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
Jie Huang'. Jie Xiao', Wenlong Zhang LEI BAI? Universiiy of Science and Technology of China 1iuy1d12023@mail edu.cn, eeuouustc.duu.cm Corresponding Author
Yidi Liu', Xueyang Fu
Dong Li'.
Zheng-Jun Zha'
"Shanghai AI Laboratory
Abstract
Ultra-High Definition (UHD) image restoration struggles to balance computa- tional efficiency and detail retention. While Variational Autoencoders (VAEs) offer improved efficiency by operating in the latent space, with the Gaussian variational constraint, this compression preserves semantics but sacrifices critical attributes specific to degradation and thus compromises recon- struction fidelity. Consequently, VAE redesign is imperative to foster a robust semantic representation conducive to generalization and perceptual quality, while simultaneously enabling effective eigh-freqencyy information processing crucial for reconstruction fidelity. To address this, we propose Latent Harmony, a two- stage framework that reinvigorates VAEs for UHD restoration by concurrently regularizing the latent space and enforcing gigh-freqqency-awee aware reconstruction constraints. Specifically, Stage One introduces the LH-VAF, which fortifies its latent representation through visual semantic constraints and progressive degrada- tion perturbation for enhanced semantics robustness; meanwhile, it incorporates latent cquivariance to bolster its high-frequency reconstruction capabilities. Then, Stage Two facilitates joint training of this refined VAE with a dedicated restoration model. This stage integrates High-Frequency Low-Rank Adaptation (HF-LoRA), featuring two distinct modules: an encoder LoRA, guided by a fidelity-orienter high-frequency alignment loss, tailored for the precise extraction of authentic de- tails from sngrradaaionsenssitii high fighffreuenccy components; and a decoder LoRA, driven by perception oriented loss, designed to synthesize perceptually superior textures. These LoRA modules are meticulously trained via alternating optimiza- tion with selective gradient propagation to preserve the integrity of the pre-trained latent structure. This methodology culminates in flexible fidelity-pereeption trade- off at inference, managed by adjustable parameter Extensive experiments demonstrate that Latent Harmony effectively balances perceptual and reconstruc- tive objectives with efficiency, achieving superior restoration performance across diverse UHD and standard-ressouttion scenarios. The code will be available at sttps://githut. cww 2022 Lattr//-aatttttaton
-oreentto--oieeteed
Introduction
Image restoration [1-7] aims to recover tigh-quality images from their low-quality degraded versions with semantic recovery and detail reconstruction, which often struggles to handle unknown corruptions in real-world scenarios. To address this, all-in-one image restoration methods (8-10) develop single model for multiple degradation types, striving for broad generalization. However, they often face computational efficiency challenges, particularly high resolutions. In contrast, Ultra-Hige Definition (UHD) image restoration [1]]66] specifically targets the immense data scale and intricate detail preservation required for 4K images. To meet complex scenes with various degradations and
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Restandion Netwrek
Vanaatonaa Autrlmeeder
H
Degradation.Aoo Awarr Branch
Standard All in
UHD All in
Ours
Latant Degulatiction
Grnerdlizaiva Capabliit
Generalizatio Capabiltt
Generdiizaion Capebility
Plomewrrk Fificiincy
Promwwork F.ficiency
Pramwook Effieeeccy
A Aduusable Perception and Fdidii
Adjusible Perception and Fidelity
Aduusabll Perception and Fidelit
Figure 1: Comparison with existing mainstream methods. Our method outperforms existing standard and UHD all-in-one approaches by leveraging latent regularization, achieving superior efficiency and generalization without requiring degradation- aaraaa branches, while enabling adjustable fidelity and perceptual quality during inference.
high resolution, UHD all-in-one image restoration [1 is developed based on the above works, amplifying the challenges of robust generalization, extreme computational efficiency, and meticulous detail fidelity.
To enhance efficiency, existing methods employ Variational Autoencoders (VAEs) to migrate the core restoration process to a lower-dimensional, compact latent space [4] [7 II [8 This approach allows downstream de-degradation networks operate on significantly smaller latent space, and finally decoding step back to the original space to reconstruct fidelity details while preserving semantics.
However, directly applying VAES to complex all-in-one UHD image restoration tasks reveals inherent limitations. The VAE's typically achieve compression through Gaussian variational inference, excels at preserving global robust semantic representations [9). Yet, this mechanism often leads to the loss of critical high-frequency attributes intertwined with degradation characteristics, restricting VAE's ability for high-fidelity reconstruction with the loss of fine details and textures affected by various corruptions. Therefore, a fundamental challenge arises: how redesign VAE mechanisms effec- tively trade-off two crucial properties: (1) extracting degradation-robust semantic representations that ensure good generalization and perceptual quality, and (2) ensuring that these latent representations. upon reconstruction to original pixel space, can adequately process and represent degradatiin-rreltted high-freou feeherryyency information for high reconstruction fidelity.
To address this challenge, this paper proposes Latent Harmony (LH), a novel two-stage synergistic framework. The core idea of the LH framework is to enable the latent representation possess both strong semantic robustness and high reconstruction capability by simultaneously regularizing the latent space and imposing high-freqquency-elattee reconstruction constraints.
The first stage introduces the LHEE as the foundation for all-in-one UHD image restoration. In its encoding process, building upon the VAE's original Gaussian distribution constraint for latent, the LH- VAE further incorporates progressive degradation perturbation and visual semantic constraints to enhance latent semantic robustness. Concurrently, during its decoding process, latent space equivari- ance constraints are introduced to improve the latent representation's 's intrinsic ability to reconstruct high-frequency components. This stage aims to construct generalized latent space resilient to various degradations and possessing a more balanced frequency characteristic for reconstruction.
The second stage is built the above VAE. This stage involves joint training with restoration model, addressing the VAE co-optimization and the perception-fideiig balance. After initially training a latent space restoration network with fixed LH-VAF, an innovative high- fethh--rruencygguueee -uided Low- Rank Adaptation (HF-LoRA) fine-tuning mechanism is introduced. To manage degradation-sena sensitive high-frequency information and enhance fidelity, Fidelity-oriented oriented HF-LORA (FHF-LORA) is intro- duced into the encoder, guided by a high-frequenc, alignment loss that aligns with the restoration model. Meanwhile, to enhance the perceptual quality of reconstructed output, pniception-orienter HF-LORA (PHF-LoRA) is incorporated into the decoder guided by a high-frequency perception loss. These LoRA modules are trained via alternating optimization with the corresponding losses, thereby protecting the pre-trained VAE's structure from potentially disruptive gradients. Further- more, the framework allows users to fiexibly balance the fidelity-oriented and perception-oriented high-frequency contributions in the final output via an adjustable parameter a during inference.
The main contributions of this paper include:
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We construct a new Latent Harmony two-stage framework, which systematically addresses the multiple trade-off challenges in UHD all-in-one image restoration.
We design a new latent space regularization strategy that combines progressive degradation. semantic alignment, and equivariance constraints to construct a high-quality generalized VAE latent space.
We propose pioneering high-frequency-guun LoRA fine-tuning paradigm that optimizes encoder LoRA for fidelity and decoder LoRA for perception, achieving synergistic solution for enhanced performance, VAE structural integrity, and controllable output characteristics.
Extensive experiments demonstrate the superiority of the proposed framework across various UHD and standard- degradation scenarios.
2 Related Work
2.1 UHD and All-in-One Image Restoration
Ultra-High Definition (UHD) image restoration focuses on recovering high-fidelity images from low-quality UHD observations [14 20/22). The primary obstacles are the prohibitive com- putational cost of processing high-dimensional data and the critical need to preserve aneeggrined, high-freeue freuunry details. Directly applying deep models in the pixel space is often computationally infeasible. Consequently, a dominant strategy is the downsample-enhance -ppsample paradigm. Meth- ods like UHDFour [23] perform 8x downsampling to enable inference on edge devices, while others like UHDformer [66 and UDX-Mixer (4 use high-resolution features or frequency modulation to guide restoration in a lower-resolution space. However, this downsampling approach inherently discards information crucial for intricate textures in UHD images, imposing an upper bound on the achievable restoration quality. An alternative, efffciency-orriente models, such as Variational Autoencoders (VAEs), to perform restoration in a compressed latent domain. For example, Dream UHD (4) employs VAE with frequency domann enhancements, and CD2-VAE [88] uses a VAE with feature decoupling to maintain background fidelity while removing degradations. These works underscore the potential of carefully designed latent space models for UHD restoration. Nevertheless, a key challenge remains: enhancing the representational capacity of the latent space for complex degradations while mitigating the intrinsic limitations of VAEs, such as the trade-off between generalization and reconstruction accuracy.
approach leverages latent space
All-in-One image restoration aims to devise unified model capable of addressing diverse, mixed, or unknown degradation types, necessitating exceptional generalization and adaptability [25] TO [0
[66 28 Unlike traditional methods that are tailored to specific degradations, All-in-One models are better suited for real-world scenarios with complex degradation patterns. Key challenges include managing degradation heterogeneity, mitigating task conflicts, and achieving awareness of unseen degradations. A prevalent architecture combines a degradation-awarc branch with a powerful image restoration backbone. The former often utilizes Mixture-of- Ex-eerrtss (MoE) (29 28 Prompting (3) while the latter typically adopts architectures like Restormer or NAFNet. For instance, PromptIR [ introduced prompt-based mechanism adapt to various degradations, and MoCE-IR 33 leverages an MoF. design for specialized expert handling. Despite their strong performance, these methods often exhibit limited efficiency, which makes full-resolution inference on UHD images computationally demanding on consumer-grade GPUs and thus hinders their practical deployment.
2.2 Variational Autoencoders and Latent Space Optimization
Variational Autoencoders VAEs are widely used in image restoration due their encoder-decoder structure, which maps nigh-dimensional images to a low-d wiw-dimnniiona latent space. The VAE objective, the Evidence Lower Bound (ELBO), creates fundamental tension between reconstruction fidelity and latent space regularity, the latter enforced by a KL divergence term that constrains the latent posterior to a prior distribution. This trade-off critical: excessive regularization can lead blurry reconstructions from an information ioformaatoo-sttrveed starved latent space, whereas prioritizing fidelity can weaken the latent structure and impair generalization. While VAEs are foundational to tasks like denoising and super-resolution and form the backbone of Latent Diffusion Models (LDMs), their inherent information compression often leads to the loss of high-frequency details significant drawback for UHD restoration. Recent works aim to mitigate this; for example, FA-VAL (34) uses frequency-
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complementary modules, and Wavelet- VAV [35 and Lit 36 leverage wavelet transforms to better preserve fegh-freeuency components.
latent space regularization To overcome the limitations of standard VAES, researchers have devel- oped advanced strategies for latent space regularization. Beyond simply tuning the KI. divergence weight as in 3-VAE [37], these include applying contrastive learning for improved discriminability (e.g., Hi-CDL in VAE (18), imposing geometric constraints on the latent manifold [38 39],
and employing diffusion-based decoders to enhance generation quality (e.g., v-VAE 40 ). Such
techniques aim make latent representations more robust to transformations like image degrada- tions. For instance, aligning VAE latents with features from powerful pre- traineaid vision models like DINOv2 411, as done VAVAE 442,, injects valuable semantic priors that improve robustness. Furthermore, methods like REPA 4 and REPA-E 44] explore feature alignment losses to optimize VAE and LDM co-training, which also refines the latent structure. These advancements suggest that integrating multiple regularization internal structural constraints promising direction for creating a latent space optimized for All-in-One UHD image restoration. This principle forms the foundation of our proposed Latent Harmony framework.
parregiess-pattcllarry by combining external priors with
3
Motivation
PSNR
SSIM
NIOE
User Study
Frozze VAF
LoRt.
(b0 Cooes Depeodation Costne SntaatyCDDC) artlsts
baselinel
Hight fatth//rraaryy (HF-LaRA) unalyst
Parogtad Loaa
Encoder LoRA
Decoder Lolt
LoRA
Iunelinee
Latent Harmony poReets acs-os
Treppary
Thgh Trogarac
Genrritatton
VLE bochhel WAl baoilac
Pxp
Benopetrnctin Fidellyy
(a) TSNE visualization for different VAE latent
(b) andlysis for different VAE Jatent
(d) Amalysi of different fine tuning losses
Figure 2: Motivation Analysis. (a) t-SNE visualization of VAE latents under diverse degradations, showing Baseline2's degradation-sensinn clustering versus our method's semantic clustering. (b) Cross-degradation cosine similarity (CDCS) analysis, with higher CDCS in high-frequency bands. (c) DCT spectral analysis, revealing Baselinel's 's low high- fieghfrreeuncy components and Baseline2's elevated components, indicating reconstruction-eeeeralizzzziii proportions. (d) Fine-tuning loss comparison, highlighting stable downstream gains with high- frequency loss. (e) HF-LORA experiments, demonstrating optimal fidelity and perceptual gains from encoder (fidelity loss) and decoder (perceptual loss) fine-tuning. (Note: All metrics in (e) are normalized to a positive scale, where higher values indicate better performance)
trade-off via latent high-frequency
3.1 Latent Space Representation: Generalization vs. Reconstruction
To inform our VAE's design for UHD all-in-one image restoration, we conducted experiments comparing two VAE baselines: standard VAE (baseline and VAE with enhanced reconstruction (baseline2, Appendix). By analyzing the Pross-Degradation Cosine Similarity (CDCS) of their latent representations for diverse degraded inputs in Fig, b)) we find that stronger reconstruction

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(baseline2) yields lower latent CDCS (i.e., more diverse among degradations), even below the input's pixel-space CDCS. The t-SNE in Fig. depicts similar phenomenon that stronger reconstruction promotes degradation-driven clustering in latent space, undermining content-based organization. These results suggest that enhancing VA reconstruction capability makes the latent space more sensitive to input degradations, posing challenges for downstream restoration networks.
We then further perform frequency-domaie analysis for latent representations Fig. b)) depicting that high-frequene exhibits low CDCS and low-frequency depicts higher CDCS. Subsequently, a more detailed frequency analysis is performed for both pixel and latent spaces, referring [45] Results in Fig. Figure ()) indicate that VAEs with stronger reconstruction capabilities (baseline2) tend to encode a signincantly higher proportion of high-frequency components their latent space compared to the pixel space. This implies that VAE with strong reconstruction ability encodes ample high-frequency information to manage the challenging task of decoding consistent fine details.
Moreover, we find that the high-frequency components inherently exhibit low CDCS (Fig. 2b) among degradations in latent. Based on the above observations, an excessive proportion of high fre- quencies is heneficial for detailed reconstruction but compromises the latent space's robustness to varied degradations. Therefore, our first core impetus is to constrain the latent space to a more moderate high-frequency proportion, balancing latent representation robust degradations (high CDCS in latent) for generalization and has good reconstruction capabilities (low CDCS in latent).
3.2 VAE Co-optimization: Downstream Adaptation vs. Structural Preservation
Following the pre-trained VAE with initial generalization capabilities, a critical question arises: should this VAE remain fixed for downstream restoration tasks, or can downstream restoration supervision signals further adapt the pre-trained VAE to improve the final restoration performance?
While co-optimizing the VAE with the downstream restoration network theoretically promises im- proved overall performance, direct joint optimization is fraught with risks. Drawing parallels from Latent Diffusion Models (LDMs), where directly applying the main diffusion loss the VAE can be detrimental [44]. We conceptualize an observational experiment, the pretrained VAE is fine-tuned by backpropagating the restoration loss LRcs from the downstream network Rg. As illustrated in Fig. dd), unfreezing the VAE yields faster initial PSNR gains compared to its frozen version; however, continued training often results performance oscillations. This phenomenon can be attributed to the direct optimization pressure of LeSS. which compels the encoder to prematurely and aggressively re- move degradations from the input. Consequently, this direct optimized paradigm tends to devolve into simplified bottleneck structure geared toward direct pixel-Ievel restoration, thereby disrupting the learned latent space structure. In contrast, while a frozen VAE ensures training stability, its lack of adaptation to the restoration task leads to performance bottleneck that cannot be overcome in later training stages.
The success of -EPA-E 44 achieves effective co-optimization using representation alignment, suggesting path. our restoration task, while Stage for VAE's generalization might reduce some high-frequency components, the final reconstruction quality depends on recovering these details. This motivates using high-frequency information alignment as a "bridge" loss for co-optimization.
In our restoration task, as discussed in Section 3.1 enhanced generalization often sacrifices some high-frequency information, while low-frequency components are effectively encoded within a struc- tured latent space. To balance the preservation of the pre-trained latent structure in VAE with performance gains in downstream restoration tasks during joint optimization, we focus the optimization objective on high-frequency components, employing nigh-frequency information alignment as a "bridge" loss for co-optimization. As shown in Fig. d), backpropagating the loss to update the VAE maintains training stability while overcoming performance bottlenecks of restoration. Conversely, using low-frequency alignment loss leads to training insta- bility. Thus, our second core impetus is to introduce high-frequency alignment loss as a bridge for joint optimizing VAE and restoration network, preserving the VAE's pre-trained, highly generalizable representations while achieving further performance improvements driven by downstream tasks.
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3.3 High-Frequenn Restoration From Latent: Perception vs. Fidelity
To address the need for task-specific VAE adaptation through high-frequency guidance (see Sec- 3.2). we focus on designing the high-frequency alignment loss and analyzing its impact on output quality. High-frrquenne detail recovery entails trade-off between perceptual quality and fidelity, Pixel-level losses (. typically formulated as Maximum Likelihood Estimation, minimize both Systematic Effect (SE) that affects fidelity via regressable components like edges, and Variance Effect (VE) that influences perception through non-regressable textures [46]. Minimizing I Lizz, including its higu-freyuency component, suppresses SE and VE, yielding high PSNR but perceptually flat outputs due to reduced VE. This is expressed as:
min 9
Ey (().))) L(u. My)] Ey.s (C(V.0) Cly 14))
SE: LF + regressable HF
VE: non-regressable HF
where C is a symmetric loss, y ~ p(yla)), 5 estimates y,, and ply 2g are their respective means.
(a) Fidelity-Oriented High-Frequency Restoration: This approach prioritizes the faithful extraction or disentanglement of authentic high-frequency components from the input signal. aligning with the ground truth Letean It emphasizes the "traccability" of high-frequency details, aiming to closely match the ground truth and achieve high fidelity metries. However, its efficacy is constrained by the availability of residual high- freuurrey information in the input. Moreover, the suppression of variance effects (VE) can result in monotonous textures, thereby limiting overall perceptual quality.
(b) Perception-Orientee
Figg-erre Generation: This strategy focuses on generating visually natural high-frequency details, which may not precisely map to the input signal but rely heavily on learned priors about natural images' appearance high frequencies. It prioritizes the visual "plausi- bility" of high-frequency information, aiming to preserve or shape VE for enhanced visual quality. However, it may introduce structural inaccuracies or hallucinations and compromise fidelity.
The analysis reveals that mechanisms targeting structural fidelity (SE reduction) and texture perception (VE preservation/shaping inherently pursue distinct optimization objectives. To address these coupled objectives, we introduce two independent Low-Rank Adaptation (LoRA) modules. Specifically, fine-tune the VAE's encoder, decoder, or both using fidelity-oriented and perception-oriented high- frequency losses, respectively, and evaluate their impact on fidelity and perceptual metries. As shown in Fig. eee). fine-tuning the VAE's encoder with the fidelity loss enhances fidelity metries with minimal perceptual quality degradation, while fine-tuning the VAE's decoder with the perceptual loss improves perceptual metrics at modest cost to fidelity.
This insight forms our third core impetus: within gigh-frequency-alige alignment--bused VAE fine- tuning framework, we propose differentiated mechanisms. One mechanism focuses on faithfully extracting and aligning regressable tigh-frequency components to enhance fidelity in VAE's encoder, while the other concentrates on generating perceptually superior non-regressable high-frequency details to improve perceptual quality in VAE's decoder. We posit that balancing these independently guided mechanisms can effectively synergize fidelity and perception.
4 Methodology
Building on Section this chapter details our proposed Latent Harmony two-stage synergistic framework. This framework, through latent space regularization in the first stage and high-frequenyy guided controllable refinement in the second stage, addresses the trade-off' between: (1) latent space generalization and reconstruction fidelity, (2) VAE co-optimization with downstream tasks versus structural preservation, and (3) the final output's 's perceptual quality versus fidelity.
4.1 Stage One: Constructing a Generalizable Latent Space Representation
The objective of this stage is to train a Variational Autoencoder (VAE) comprising an encoder Eg and decoder D.. such that it learns latent space Z robust to various degradations. The base training follows the standard VAE objective, optimizing a loss AV that includes an L1 reconstruction loss on clean images Tetnon and KL divergence regularizer:
LV D
Lotean 1l + AKL KL[gs(z Ietan) [[))]
(2)

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-08
Better Fideliity
PSNR
SSIM
NTQF
User Stady
Better Perception
Stage
Frecon
Two
DINOWZ
PHF-LoRA
PHE--ORA
PDPS
Line
LyAE
Sowe
Raxal
His
Zeae
dea
Scale
loop
Teteon
transtommaionn
Lyngo
Zeown
Shand Flow
Figure 3: Framework Overview. Stage I: LH-VAE training employs progressive degradation perturbation, degradation-invariant visual semantic loss LINV, and latent space equivariance loss E construct robust, generalizable latent space. Stage 2: Latent space restoration leverages Rg and high-frequenee rrequency-guided guided LoRA fine-tuning, with Fidelity-oriented HF-LoRA (FHF-LoRA) for the encoder and Perception-orientél HF-LoRA (PHF-LoRA) for the decoder, enabling adjustable fidelity and perceptual quality via parameter a during inference. Results tuning are shown in the upper panel, with metrics normalized positively, where higher values indicate better performance.
To counteract the standard VAE latent space's sensitivity to degradations, particularly in high- frequency components, we introduce progressive degradation perturbation strategy(PDPSS, During training, increasingly severe degradations are applied to Telemn over time t. This perturbation is probabilistic and can take one three forms: no perturbation, synthetic degradation, or interpolation with paired real degraded image laeg. The severity of synthetic degradations is controlled by an increasing function sev(t), and the interpolation with lazg is controlled by an increasing coefficient 3()). Formally, the perturbed image lag is generated as:
Jetean
with probability with probability - ((ta) + B(t) with probability P2
lang
SynthDeg( (ettems sev(t))
where Po + =1 SynthDeg(/, (eveDee((.see())) sev(t)) applies a randomly selected set of synthetic degrada-
tions (e.g., Gaussian noise, blur. JPEG compression) to image I, with their severity controlled by sev(t), monotonically increasing function of t. The interpolation coefficient ((t) E [o. 1] is also monotonically increasing function of t. signifying progressively stronger influence of the paired degraded image. This progressive approach ensures learning stability.
On this basis, two key regularization losses incorporated. The degradation invariance visual semantic loss LYNV leverages semantic features Fy VFM extracted from a pre-trained DINOv2 [4] model reference, enforcing the encoder Es align the encoding <'dng Eg (Paeg) of the perturbed image with this reference, learning degradation invariant content representation:
Line = di d(='aeg> fVFM)
(4)
where .) denotes a distance metric in the feature space. Additionally, the latent space equivariance loss Ly constrains the consistency between the decoded result of randomly downsampled latent encoding Zdown Downs(Zelean) and the corresponding downsampled image I Down,(Letean enhancing scale robustness and reducing reliance on high-frequency components:
LEgu a (Zdown) Idown ||
(5)
The joint optimization objective for this stage combines these terms as:
Lstagel = LV AIav + \Bqu
(6)
Optimizing this objective yields VAE parameters (b*) that define a latent space exhibiting stronger cross-degradation consistency and more balanced frequency characteristies, establishing
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Histlomer:
Gridformer
GT
Histionner
Cnetiomer
Gi
Low light cilinngemeet
UIID processes
Laage dchazing
HAIR
processer
Histformer
Gridformer
Histfommer
Grdifornur
Image deblurting
HAIR
UHD-ppocesser
Ours
denoising
HAIR
URD-proeser
Ours
Figure 4: Visual results for four types of degradation removal with other all-in-one methods.
generalizable foundation for subsequent restoration, albeit potentially at the cost of reducing high-frequency information useful for reconstruction.
4.2 Stage Two: High-Frequency Guided Controllable Low-Rank Adaptation
This stage aims to achieve high-quality image restoration using the generalizable latent space (6" 15*) from Stage One, while compensating for lost gigh-frequency details and providing controllability the final output. Initially, latent space restoration network Ro is introduced, which processes the encoded degraded latent zdeg = Eg- (Tdeg) to predict a restored latent 2res = Ro (o(de))) This network is trained solely with standard restoration loss, keeping VAE's parameters (ø", v/ frozen:
LRea
D V- (Zres) Lotennl1
Gradients update only the parameters 0 via 0 0 V
Subsequently, to finely restore high- freuurrey information without compromising the acquired generalization, bigh-frrqquncy--uiiee
Low-Rank Adaptation (HF-LoRA) fine-tuning is applied to pre-trained VAE. Low-rank updates AdLORA and A are introduced to the base param- eters o= and t/", such that Φ &² + AÓLONA and s/b = x²* + AtLLONA- The LoRA parameters OLoRA (AoLORA: A are optimized solely by specific high-frequency alignment loss LHF, decoupled from the main restoration loss Leee to preserve the latent space structure learned in Stage One. We design Fidelity-oriented HF-LoRA (FHF-LoRA) for the encoder, guided by high-frequency alignment loss to enhance fidelity, and perception -oriented HF-LoRA (PHF-LoRA) for the decoder, guided by high-frequene frequency perception loss to improve perceptual quality, with both modules trained using alternating optimization strategy.
When optimizing the FHF-LORA (DCLORA). the decoder uses its frozen base parameters s/" with the objective being high-frequency fidelity loss LHFFa. that extracts high-frreueenc structures from the degraded input consistent with the ground truth:
LHFria
|HF (Dp- (Es- +A0ZORA (Ideg))) HF((eeea))
(8)
When optimizing the PHF-LORA (AWA)))) the encoder uses its frozen base parameters d', with the objective being high-frequency perceptual loss LHFrere generate visually natural and sharp high-frequency textures, enhancing perceptual quality. This loss is implemented as a GAN-based loss LHFEAN minimizing the adversarial loss for the generator (decoder LoRA) to deceive high- frequency discriminator DHF. The discriminator DHF optimized by adversarial loss:
LHFGAN =
Era
[log DHF (HF (D0+440rea (Ro (E (Jaeg) ))))]
4.3 Inference-Time Control
The differentiated LoRA modules, trained with distinct losses and alternating optimization CimmmizzationnooooRR A for fidelity extraction and AdLORA for perceptual cetneration-prviiie flexibility during inference. Users can introduce control parameter encoder and decoder LoRA modules to the final result, for instance, via Φ O" + aAÓLoRA and //* (1 A)ALLARA This mechanism enables a tailored trade-off between maximizing fidelity and optimizing perceptual quality, depending on application requirements.
[0, 1] dynamically adjust the contributions of the
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Table 1:
Comparison to state-of-the-art four degradations. PSNR (dB, 1), SSIM (1), and LPIPS (L), and FS represents full-size 4K image inference. FLOPs are computed for an input size of 256 256. Best and second best performances are highlighted.
Methed
IS FLOPs Params.
Low Light
Detturring
Deboning
Donwising
Average
UHDDL
UHHD-bler
UHD-hhsee
UHIN m5
UHDN ,:25
UHDN
70:50
9M
.509
7757
21.33
20.78
.784
18.79
.75
20.07
.2543
TDR
27
X
88G
15.3M
23.12
910
24.67
793
19.12
768
48
912
25.86
.872
24.57
654
24.14
.822
.2684
PromgtIR
158G
13M
23.44
9022
25.77
782
19.97
722
28.43
993
26.74
898
23.72
584
24.68
.033
.2571
CAPTNe:
3U
25G
24.3M
23.96
798
25.58
23.39
.346
NDR-Restowe
196G
36.9M
23.84
894
24.25
802
20.08
892
23.62
912
24.37
22.94
669
23.52
846
3126
Gaidforma
X
367G
14M
23.12
898
25.82
785
19.24
869
36,04
933
31.72
898
26.24
623
27.03
836
.3754
D 19
10G
36.2M
21.56
812
23.85
743
18.28
864
36.84
938
32.42
.977
26.08
648
26.51
818
2561
Histoformer
26.88
adaiR 10
147G
28.7M
23.57
916
18.44
901
32.84
931
30.48
901
26.48
672
26.36
HAIR 81
41G
29M
25.75
922
25.78
798
20.00
894
35.54
916
30.84
888
26. 26
657
27.36
847
.622
UHDpprocsser
4G
1.6M
27.11
925
26.48
8033
20.94
922
38.94
975
33.99
903
27.95
.677
29.33
868
.2541
3.6G
Table 2: Comparison to state-of- on six degradations. PSNR (dB, 1), SSIM (1), LPIPS ())
and FS represents full-size 4K image inference. FLOPs are computed for an input size of 256 256. Best and second best performances are highlighted.
Method
FLOP:
Params.
Eigha
Deblurring
Delvecing
Denniing
Demmning
Average
UIID-LL
ULID-blur
UTID haze
UTIDN
UtID-rain
UII
AIRNaI
265
301G
9M
22.68
.587
23.52
876
18.24
846
22.38
.876
26.35
.876
27.38
934
23.43
.874
.1861
IDR
15.3M
91.5
778
20.64
PRompeIR
158G
33M
23.3
.111
25.48
805
20.14
901
24.38
833
28.59
.897
30.78
966
25.74
.36
.2155
CAPTNe
10
X
25G
24.3M
24.97
921
26.33
795
20.32
.03
21.64
.560
29.34
908
32.21
974
25.80
845
2861
NDR. Ristor
1966
36.99 16
25.12
585
25.64
791
19.21
896
31.44
915
29.34
.977
28.41
948
26.51
889
3105
Gridformer
1R
34M
DiTUIR-1
10G
36.2M
22.64
902
25.08
785
18.62
33.25
998
27.80
886
27.
945
25.81
1844
Hiseofomerr
X0
X
91G
16.6M
25.73
915
26.55
.76
18.73
897
33.05
.94
27.96
884
27.56
971
26.59
898
1855
adilR [00
147G
28.7M
23.84
918
26.86
803
19.34
.100
32.46
923
28.18
.001
27.72
953
26.40
.2492
39M
50
UHDDyeeesseer
1.6M
26.91
934
26.95
.017
21.81
931
33.73
834
29.90
.915
32.73
979
25.67
Ours
3.6G
1.2M
27.14
925
27.21
815
22.32
936
34.17
942
31.41
.919
33.24
.82
29.24
.920
.1822
5
Experiments
5.1 All-in-One and Single-Tast Task Restoration on UHD Scenes
We evaluated the efficacy of our proposed method for the UHD all-in-one restoration task across two experimental configurations: four four-degradation and six-dggraattion settings. As reported in Tables [T 2nd our approach consistently achieved state-of-the-ar performance in both settings while main- taining optimal computational efficiency. Fig. shows visual results of the four-degradation setting, depicting that our method effectively removes degradations while preserving intricate background textures. Although method is designed for all-in-one tasks, it does not significantly compromise single-task restoration performance, as detailed in the supplementary.
5.2 Adaptability and Application Exploration on Standard- Resolution Scenes
Our method employs a unified processing strategy for all degradations, eschewing specialized degradation-awar branches, thereby achieving superior generalization compared to traditional ap- proaches. We validated this generalization capability under two experimental settings: unseen degradations excluded from training and novel composite degradations formed by combining trained degradation types. As shown in Table our approach significantly enhances generalization per-
Table 3: Adaptability in -esoluttn Scenarios. Catiiiossoommmooooooom use LPIPS and FID scores, with lower values indicating superior performance.
Type
Method
Haze
Rain
Snow
Motion Blur
Raindrop
Low Low-light
Diserimiinatvv--aaad
ProcuptR ProcigeRR Ours
0.305/141.05
0.97/92.61
0.100/1.334
0.163/35. 79
O. 0.189/94444
0.423/189.8
0.224/121.12
0.086/8.008
0.092/17.12
0.161/35.12
0.182/76.84
0.578/172.59
based
Didl Plugin
0.340/143.66
0.165/39.71
0.178/18.08
68
0.185/60.64
0.4664167.63
Dill Plugin/w Ours
0.321/131122
0.162/39.44
0.174/18.02
0.138/35. 42
0.146/64,226
0.432/152.26
VAE-hused
CosAE
Ti
0.338/188.7
0.146/38.22
0.162/16.78
0.186441 78
1118/449.22 27
482/1122224
CosAE/ Churs
0.224/126 28.
0,098/28.77 799
0.121/1 1.55
0. 0.16R/36 22
0. 0.119440.2
D. 0.582/159.8

---

Table 4: Generalization Verification. PSNR (dB, 1), SSIM (t), and LPIPS (4) are reported.
Method
Univen
Composie Degradation
UlID-raim
UtID-anow
UHID muire
LLIE+Noise
Hize+LLIE
Noise-Blur
HAIR
00.924
0.392
25,43
0.903
0.223
17.28
0.798
0.446
18.
0.812
0.492
16.72
0.862
0.49
0.854
0.41
UHDDggoocsser
22.72
0.934
0.342
21.82
0.918
0.267
14.32
0.778
0.480
15.28
0.842
0.425
12.38
0.872
0.462
18.28
0.344
0.492
Ours
28.13
0.892
0.233
28.92
0.967
0.184
19.26
0.898
0.326
20.33
0,882
0,342
19,82
0.904
0.328
24.28
0.898
0.278
formance in both scenarios, demonstrating that the homogeneous latent space processing paradigm proposed in this work is more robust alternative to incorporating degradation-awar branches.
The primary objective of this work is to develop a VAE framework tailored for UHD restoration tasks. However, VAEs are also widely employed in standard-resolution scenarios to enhance perceptual quality and reduce the computational demands diffusion- based restoration methods. To demonstrate the versatilityy of our approach, we integrated our proposed LH- H-VAE with three representative standard- resolution restoration methods: discriminative- based, Latent Diffusion Model (LDM)- based, and VAE-based. Experiments were conducted on a mulli-degradatiin benchmark curated from the Gendeg dataset. As shown in Table 3 our method significantly improves the perceptual metrics of all three approaches in standard-resolution settings, thereby validating its generalizability.
5.3
Ablation Studies
Table 5t Ablation Studies. Studies.Comprent Comprehensive ablation experiments validate the efficacy of our approach.
(a) Ablation study of Lateat Harmony.
(c) Ablation Study Latent Restoration Network.
Configuration
PSNR
SSIM 1
LPMPS L
Restormer
NAPNet
SF
Latent Hammony
29.77
0.88
0.250
w/o Lis
24,28
0.79
0.292
PSNR (dB)
24.22
29.73/ +5.51
24.63
29.68/
+5.05
24,54
29.70V +5.16
WAo Legy
25.68
0.82
(1302
Param (M)
26.1
3.8/ 85%
29.1
1.91
84%
PDPS
0.287
PLOPS (G)
140.9
0.2/
95%
16.1
4.31
51.0
3,6/ 93%
WAo FHF-LORA
28.12
0.86
0.386
Ruutame (8)
888
0.62/ 92%
4.6
0.417
.258
5.2
0.43/ .2%%
Who PHR-LoRA
29.02
0.84
0.306
KS
weo LoRA Fine-Tuning
28.68
0.85
0.298
Fine-Tuning
28.48
0.202
(d) Performance metrics under different values.
Metric
0.2
0.4
0.6
0.8
(b) Inference time comparison.
PSNR
28.94
29:28
29.70
29.74
DreamUIR
Histformer
(HDDprocseer
1 (Ones)
SSIM
0.962
0.367
0.577
0,878
LPIPS
0.2218
0,2483
0.2502
0.2904
12,3
8.4
1.2
0.43
User
9.2
7.8
6.2
4.8
To validate the contributions of the key components in our "Latent Harmony" framework, we con- ducted thorough ablation experiments on the UHD all-in-one restoration task, systematically removing or modifying individual components and assessing their impact on performance using PSNR, SSIM, and LPIPS metrics. Results are summarized in Table [ The ablation of primary components, presented in 5j) confirms the effectiveness of each proposed element. Additionally, the latent space restoration network in Latent Harmony adopts SFHformer (5) and we verify the robustness of our approach across alternative network architectures in Table 5(c). Runtime comparisons, shown in Table(5yb) demonstrate significant efficiency gains over competing methods, underscoring the necessity of eliminating degradation-awe -aware branches. The impact of the tuning parameter a on fidelity and perceptual quality is illustrated in dable where increasing a enhances fidelity metries the expense of perceptual metrics, validating the tunability of our method's 's output. Detailed experimental setups, implementation specifics, and additional results and analyses are provided in supplementary.
6 Conclusion
This paper addressed critical VAE-based trade-offs in UHD all-in-one image restoration, encom- passing latent generalization versus reconstruction, structural integrity during co-optimization, and the perception-fidelin balance. We introduced Latent Harmony, a two-stage framework. Its first stage constructs robust Latent Harmony VAE (LH- VAEE) via principled latent space regularization. The second stage features high- ffreuuencygguicee LoRA fine-tuning, distinctly optimizing encoder LoRA for fidelity and decoder LoRA for perception, while preserving VAE structure. An inference parameter enables explicit fidelityperreeption control. Extensive experiments validated Latent Hurmony's 's superior restoration performance and effective balancing of these trade-offs across diverse scenarios, presenting a promising advancement for UHD image restoration.
10

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Acknowledgement
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 62225207, 62436008, 62422609 and 62276243.
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Reviews:
[{"review_id": "lkNcHw11Pu", "reviewer": "Reviewer_tSuv", "summary": "This paper introduces Latent Harmony, a two-stage unified framework for UHD image restoration that addresses key limitations of Variational Autoencoders (VAEs) in balancing semantic generalization and high-frequency detail reconstruction. Stage 1 develops a regularized VAE (LH-VAE) using progressive degradation perturbation, semantic alignment (via DINOv2), and latent equivariance to learn a robust and frequency-balanced latent space. Stage 2 applies a novel high-frequency-guided LoRA (HF-LoRA) mechanism, separating fidelity- and perception-oriented fine-tuning paths for the encoder and decoder, respectively.", "questions": "1. While the two-stage design is central, the interplay between the Stage 1 VAE latent structure and Stage 2 LoRA tuning (especially how much the LoRA fine-tuning modifies or respects the original latent semantics) is not clearly quantified.  Please provide quantitative or visual analysis (e.g., latent feature similarity, latent traversals) to show how the LoRA updates preserve or adapt the semantic structure from Stage 2.\n\n2. In Section 5.1, the authors claim that their method \"effectively removes degradations while preserving intricate background textures.\" However, the qualitative results in Figure 4 do not convincingly support this claim. In fact: The visual improvements over other baselines (e.g., HAIR, UHDprocesser) appear marginal at best;  Some restored outputs from your method still exhibit blurring, noise, or residual artifacts, especially in complex background regions;\n\n3.In Tables 1 and 2, only one other UHD-capable method (UHDprocesser ) is included for direct 4K full-size evaluation. However, recent works such as DreamUHD, and ERR have demonstrated UHD restoration capabilities, though perhaps indirectly or under modified setups.\n\nLiu, Yidi, et al. \"DreamUHD: Frequency Enhanced Variational Autoencoder for Ultra-High-Definition Image Restoration.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 6. 2025.\nZhao, Chen, et al. \"From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n4.Could you explain how Param and FLOPs are decreased in Table 5(c)?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality: This work presents a  VAE-based framework for UHD image restoration. The authors first justify their motivation through statistical analysis, then detail the loss function and network architecture. Extensive experiments validate each component, demonstrating a complete methodology.\n\nClarity: UHD image restoration combined with VAEs is natural and well-motivated, and the paper presents a clearly structured framework. To enhance readability, the dimensions of certain figures (e.g., Figures 1 and 4) could be further optimized.\n\nSignificance: The study constitutes a valuable engineering investigation of VAE-based approaches, although many design elements (Latent Space Representation and LoRA) follow an incremental improvement paradigm.\n\nOriginality:  Its novelty remains limited as it fails to provide new fundamental insights for UHD image restoration. The observed generalization capabilities appear to derive primarily from the inherent properties of VAEs.", "was_revised": true, "final_justification": "While the main components, such as VAE regularization, LoRA fine-tuning, and loss design, are based on existing paradigms, I appreciate the significant effort made to adapt these for UHD image restoration. The authors have also clarified minor errors in the figures and addressed issues in the manuscript during the rebuttal phase, providing additional experiments for further validation. Based on their response, I will raise my overall evaluation.", "rebuttal": "### W1: Figure Size\nWe will revise the size and layout of our figures in the revision to improve readability.\n\n### W2: Insight and Novelty\nWe thank the reviewer for evaluating our work as a \"valuable engineering investigation.\" However, we wish to clarify that our core contribution lies not in inventing isolated technical components, but in providing a new analytical perspective and a systematic solution for the complex task of UHD All-in-One (UHD AIO) image restoration. We believe this goes beyond \"incremental improvement.\"\n\n**Our Core Insight:** Breaking the UHD AIO Performance Bottleneck from the Perspective of VAE Latent Space Optimization.\n\nThe complexity of the UHD AIO task stems from the need to simultaneously address the demand for computational efficiency due to ultra-high resolutions and the challenge of joint optimization across multiple degradation types. Traditional methods typically devise separate solutions for these problems, but the combination of such isolated designs often leads to compatibility issues and suboptimal performance (i.e., the whole is less than the sum of its parts).\n\nOur work is the first to unify these problems from the perspective of VAE latent space optimization. Through extensive observational experiments presented in **Fig 2**, we have distilled the challenge into a new analytical framework centered on the trade-offs among three core elements:\n\n1. **At the Latent Space Level:** The trade-off between content \"Generalization\" and detail \"Reconstruction Capability.\"\n\n2. **At the Optimization Level:** The conflict between \"Fine-tuning Adaptation\" for downstream tasks and \"Structural Preservation\" of the VAE prior.\n\n3. **At the Restoration Result Level:**  The competition between the \"Fidelity\" and \"Perceptual Quality\" of the decoded image.\n\nTo our knowledge, this is the first work in the UHD AIO field to **unify the task's multiple challenges into the perspective of latent space optimization** and to further deconstruct them systematically into **three core trade-offs** through experimental analysis. This points to a **new analytical perspective and optimization direction** for the field, which we consider a key insight. Based on this, the Latent Harmony framework we propose is not a simple combination of techniques, but a systematic solution precisely designed to address the aforementioned trade-offs.\n\n**Our Innovative Solution:** A Synergistic Framework for Resolving a Triad of Trade-offs\n1. Addressing the \"Generalization vs. Reconstruction\" Conflict: **Objective-driven VAE Regularization (LH-VAE)**\n\n     Our work transcends the mere application of a standard VAE. Through comprehensive analysis in Section 3.1 (**Fig 2a,b,c**), we introduce a novel frequency encoding perspective that reveals the inherent conflict in standard VAEs: the trade-off between latent space “generalization” and “reconstruction” capabilities for the UHD AIO task. Leveraging this insight, we propose the Latent Harmony VAE (LH-VAE), which employs a joint regularization strategy integrating the Progressive Degradation-aware Pretraining Strategy (PDPS), semantic alignment loss $L_{Inv}$, and latent equivariance loss $L_{Eqv}$ . This objective-driven design reshapes the VAE's latent space to simultaneously meet the dual demands for robust generalization and detailed reconstruction, harnessing the VAE's intrinsic properties for the specific challenges of UHD AIO task.\n\n2. Addressing the \"Adaptation vs. Preservation\" Conflict: **Decoupled Co-optimization via a High-Frequency \"Bridge\"**\n\n    To address the inherent conflict between “performance adaptability” and “structural preservation” when fine-tuning VAEs for downstream tasks (see **Fig 2d**), we propose an innovative decoupled collaborative optimization strategy.This approach leverages a high-frequency alignment loss as a “bridge” to separate the VAE’s optimization from the gradient flow of the primary restoration loss. It preserves the VAE's generalization capability by protecting the stable, low-frequency semantic structure, while simultaneously adapting to the downstream task by precisely compensating for the suppressed high-frequency details. This strategy enables efficient and stable adaptation of the VAE, breaking through the performance bottlenecks of the first stage.\n\n3. Addressing the \"Perception vs. Fidelity\" Conflict: **An Objective-Decoupling LoRA Fine-tuning Paradigm (HF-LORA)**\n\n    Drawing on the differential fine-tuning effects observed in the VAE encoder and decoder (**Fig 2e**), we architecturally decouple the conflicting objectives of perceptual quality and fidelity:\n\n    * Encoder for “Extraction”: We employ a Fidelity-oriented HF-LORA (FHF-LORA), which focuses on faithfully extracting traceable high-frequency structures from the input .\n    * Decoder for “Generation”: We use a Perception-oriented HF-LORA (PHF-LORA), which excels at generating visually plausible and sharp high-frequency textures .\n    * Collaboration and Control: Alternating optimization mitigates gradient conflicts, while an adjustable parameter α provides flexible trade-off control during inference, empowering users to tailor outputs to specific needs.\n\nOur work delivers a dual contribution to the UHD AIO task. First, it reframes the task’s core challenges by systematically distilling them into three fundamental trade-offs within latent space representation optimization. Second, our proposed Latent Harmony framework is not a collection of isolated components but a synergistic system meticulously designed to address these trade-offs. This framework effectively tackles challenges at each level while achieving a remarkable synergistic effect, significantly enhancing the overall performance of UHD AIO. Consequently, we believe our work offers a robust and novel contribution to the field, both through the depth of its problem analysis and the ingenuity of its framework design.\n\n###  Q1: Quantitative and Visual Analysis of HF-LoRA Fine-Tuning\n**Visualization Analysis:** The visualization results, as shown in **Supp Fig 5** (columns 5 and 6), provide a comparative analysis of the latent representations before and after HF-LoRA fine-tuning. These results demonstrate that post-fine-tuning, the fundamental semantic and structural information is preserved, while significantly enhancing high-frequency texture details.\n\n**Quantitative Metrics:** We quantitatively evaluated the interplay between Stage 1 and Stage 2 latents by computing the Cross-Dataset Consistency Score (CDCS) and the high-frequency energy ratio (HFER). The experimental results indicate that after HF-LoRA fine-tuning, CDCS exhibits only a marginal decrease, while the high-frequency energy ratio shows a substantial increase. These findings quantitatively confirm that HF-LoRA effectively enhances high-frequency information beneficial to restoration outcomes while maintaining the integrity of semantic information.\n\n|        | CDCS | HFER |\n| - | - | - |\n| stage1 | 8.4  | 15%  |\n| stage2 | 8.1  | 23%  |\n\n###  Q2: Fig 4 Visual Results\n\nWe thank the reviewer for their insightful evaluation of Fig 4’s visual results and valuable feedback. While some improvements may seem subtle in small-scale figures, we highlight key advancements, supported by quantitative metrics and user studies, to demonstrate our method’s effectiveness.\n\nTask-Specific Advantages: In low-light enhancement, our method restores sharper background text details, unlike blurrier outputs from competing methods. For dehazing, it more effectively removes haze, recovering authentic contours and colors of distant buildings. In deblurring, it enhances human outlines with reduced ringing artifacts. For denoising, we apologize for an error in Fig 4, where the “Ours” result was mistakenly replaced with UHD-Processor’s, causing identical outputs. Corrected results, verified internally, will be updated in the revised manuscript to showcase superior facial detail restoration. Comprehensive comparisons, including denoising, are in **Supp Fig 1 and 2**.\n\nQuantitative Superiority: Tables 1 and 2 confirm our method’s consistent outperformance across all degradation tasks in PSNR, SSIM, and LPIPS.\n\nSubjective Perceptual Advantages: A user study with 20 volunteers (**Supp Fig 3**) shows our restored images received significantly higher subjective ratings than baselines.\n\nIn summary, our method achieves clear advancements in degradation removal, texture preservation, and human perception alignment, validated by visual comparisons, quantitative metrics, and user studies. We will include higher-quality figures with magnified details in the revised manuscript to better highlight these strengths.\n\n### Q3: More UHD Baseline Comparisons\n\nWe have supplemented our results with comparisons to DreamUHD and ERR, as presented below. Since these methods are designed for single-task UHD image restoration, their performance is suboptimal in the All-in-One scenario. This underscores the necessity of tailored designs for the UHD All-in-One task.\n\n|Method|DreamUHD|ERR|Ours|\n|-|-|-|-|\n|PSNR|27.2|27.8|29.7|\n|SSIM|0.853|0.836|0.877|\n\n### Q4: Reasons for Parameter and FLOPs Reduction in Tab 5c\n\nThe substantial reduction in parameters and FLOPs in Tab 5c arises from our VAE framework’s key strength: shifting computations of heavy restoration networks (e.g., Restormer, NAFNet, SFHformer) from pixel space to an efficient latent space. The process is as follows:\n\n* Encode: Our lightweight LH-VAE encoder compresses high-resolution images into compact latent representations.\n* Latent Restoration: The restoration network processes these low-dimensional latents, significantly reducing computational complexity tied to input size and channels.\n* Decode: The LH-VAE decoder reconstructs the restored latents into high-resolution images.\n\nThus, the “+Ours” metrics reflect the combined cost of LH-VAE and the latent-space restoration network, achieving 80%–95% efficiency gains while preserving performance."}, {"review_id": "qc7hjjTk7k", "reviewer": "Reviewer_KgHd", "summary": "This paper proposes a high-resolution image restoration method called Latent Harmony. It aims to address a issue: how to ensure computational efficiency while retaining details when inpainting ultra high resolution images such as 4K. To achieve this goal, the paper proposes a two-stage approach to handle the existing defects of VAEs, optimizing the VAEs by introducing an architecture sensitive to high-frequency details, followed by fine-tuning through a LoRA (Low-Rank Adaptation) module guided by high-frequency information.", "questions": "- In Section 3, the authors conduct extensive analysis of the VAE. What is the configuration of the VAE with stronger reconstruction capability? What are the design and training differences between the two baseline models? Can it be proven that low-frequency or high-frequency information in the low-dimensional latent space is strongly correlated with the low/high-frequency information in the corresponding model results in the pixel domain?  \n- Since this paper aims to solve high-resolution image restoration (e.g., UHD resolution), why are the metric calculations in Tables 1 and 2 only applicable to 256×256 resolution?\n- Different restoration tasks have varying dependencies on low/high frequencies, but the method's design still focuses on recovering high-frequency information. Intuitively, its generalization to multi-type degradation tasks relies on Equation 3, which is equivalent to moving the so-called \"second-order degradation\" from the image domain to the latent space. Has the method made specific designs for low-frequency selection or frequency adaptation, or analyzed the effectiveness of each proposed stage for each task?", "limitations": "The limitation of proposed method is discussed in the supplementary material.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n- The proposed method has a relatively lightweight computational complexity and parameter count compared to similar approaches, making it suitable for handling high-resolution content.  \n- The proposed modules enable the inpainting model to adapt to various types of degradation.  \n- Experiments show that the method can achieve superior results in metrics such as PSNR and SSIM.  \n\nWeakness\n- Figure 4 is best accompanied by a comparison of more detailed regions, such as local textures.\n- It is recommended to give a separate comparison for the restoration of text-type content in Figure 4.", "was_revised": true, "final_justification": "Please see the comment.", "rebuttal": "### W1 & W2: Fig 4 Layout\nWe will revise Fig 4 in the revision to enhance the visual comparison of regions and text, ensuring clearer presentation of the results.\n\n### Q1: VAE Baseline Configurations and Frequency Correlation Between Latent and Pixel Domains\n\n1. Detailed VAE Baseline Configurations\n\n    The configurations of the VAE baselines are detailed in Section B.2 (\"Baseline Construction\") of the Supplementary Material. For clarity, we summarize the key distinctions below:\n\n    VAE₁ (Stronger Generalization): Designed to encode robust semantic information, this model uses a KL loss weight of ($\\lambda_{KL} = 1 \\times 10^{-4}$), a downsampling factor of 32, and 8 latent channels. The higher KL weight and compression rate promote smoother, semantics-focused latent representations.\n\n    VAE₂ (Stronger Reconstruction): Optimized for comprehensive image reconstruction, this model employs a KL loss weight of ($\\lambda_{KL} = 1 \\times 10^{-8}$), a downsampling factor of 8, and 32 latent channels. The minimal KL weight and lower compression rate enable retention of high-frequency details for precise reconstruction.\n\n2. Frequency Correlation Between Latent and Pixel Domains\n\n    * The relationship between the spectral distribution of the VAE latent space and the decoded image has been discussed in detail in [1, 2]. The latent space’s spectral distribution directly influences the frequency distribution of the output image, though this relationship is non-linear. As noted in [1], the VAE decoder exhibits a spectral bias that attenuates high-frequency components, as reconstruction-oriented VAEs tend to encode excessive high-frequency information, introducing noise. This is evidenced by the latent space visualization of our baseline model VAE₂, designed for enhanced reconstruction, in Supplementary **Figure 5**. Experimental results in **Figure 2(c)** further demonstrate that VAE₂ encodes a significantly higher proportion of high-frequency components in its latent space compared to the generalization-focused  VAE₁, which exhibits lower high-frequency content. This clearly indicates a direct correlation between the latent space’s frequency composition and the output image’s characteristics (e.g., high-fidelity, detail-rich features). To illustrate this relationship, we provide PSNR metrics for the low-frequency (LF) and high-frequency (HF) components of the reconstruction outputs for both baselines, as shown below. The comparable LF-PSNR and primary differences in HF detail recovery confirm the **direct link between latent space frequency content and reconstruction quality**.\n\n   * Consequently, our findings reveal that **effective high-frequency information** in latent space promotes richer high-frequency details in reconstructed images, but a significant portion of this information constitutes **invalid noise**. To address this, our proposed equivariance loss $L_{Eqv}$ balances the latent space’s spectral distribution, reducing reliance on excessive high-frequency signals and indirectly filtering out invalid high-frequency components. This effect is demonstrated in the fifth column of **Supplementary Figure 5**, while **Supplementary Fig6**’s high-frequency truncation experiment further confirms the robustness of $L_{Eqv}$ in mitigating high-frequency attenuation.\n\n    | Baseline | PSNR(LF) | PSNR(HF)  |PSNR |\n    | - | - | - | - |\n    | $VAE_{1}$ |39.4|18.2| 24.3|\n    | $VAE_{2}$ |39.8|32.9|36.4|\n\n[1] Simpler is Better: Spectral Regularization and Up-Sampling Techniques for Variational Autoencoders [ICASSP 2022]\n\n[2] FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space[NIPS 2024]\n\n---\n\n### Q2: Image Resolution for Metrics Computation in Tables\nThe FLOPs reported in Tables 1 and 2 were calculated at a standard resolution of 256×256 to ensure a fair comparison of computational complexity with baseline methods, a common practice in image restoration. Many state-of-the-art (SOTA) methods cannot process full 4K images on consumer-grade GPUs, necessitating a unified, smaller resolution to evaluate inherent model efficiency, consistent with UHDProcessor metrics. To further demonstrate our method’s performance, we conducted additional tests on a high-memory GPU (A100 80GB) at a larger resolution (1024×1024). The results, presented below, highlight our method’s pronounced **efficiency advantage in higher-resolution scenarios**.\n\n| Method   | PromptIR | Histoformer | Gridformer | AdaIR | UHDprocessor | Ours |\n| -------- | -------- | ----------- | ---------- | --------- | ------------ | ---- |\n| FLOPS(G) | 2530     | 1465        | 5878       | 2354      | 89           | 59   |\n\n---\n\n### Q3: Frequency Adaptability and Task-Specific Analysis \n\nThe reviewer accurately notes that different tasks exhibit varying dependencies on frequency components. Our framework addresses this through the synergistic interplay of two stages, rather than solely relying on high-frequency restoration, ensuring a degradation-agnostic approach with robust generalization.\n\n* **Stage 1 - Universal Foundation:** Stage 1 is designed to construct a robust, frequency-balanced latent space foundation that is not limited to high-frequency components.\n\n    * Low-Frequency Semantic Preservation: The Progressive Degradation-aware Pretraining Strategy (PDPS, Eq. 3) and the degradation-invariant visual semantic loss ((L_{Inv})) prioritize preserving low-frequency semantic structures, enabling the model to capture core semantic information agnostic to degradation types. This generalization capability stems not only from the multi-degradation adaptability of PDPS (Eq. 3) but also from its synergy with (L_{Inv}), which explicitly enhances degradation-invariant encoding in the VAE.\n    * Frequency Balance: The latent equivariance loss ((L_{Eqv})) promotes consistency across different scales, reducing over-reliance on specific frequency components and fostering a balanced frequency representation in the latent space.\n\n* **Stage 2 - Controlled Fine-Tuning:** The focus on high-frequency restoration in this stage addresses the inherent loss of high-frequency information during VAE encoding, rather than task-specific high-frequency dependencies. To preserve the robust, balanced latent space from Stage 1, we introduce HF-LoRA for fine-tuning. This approach enhances the extraction of high-frequency details critical for restoration tasks while maintaining the integrity of the latent structure.\n\nAlthough different degradations emphasize distinct frequency bands for restoration, our method’s Stage 1 leverages **degradation invariance** and **frequency balance** to enable the VAE to extract **semantically relevant information** across all degradations. Consequently, our approach is **degradation-agnostic**, requiring no specific designs for particular degradation types, as evidenced by the significant performance improvements in **Tables 1 and 2**. Furthermore, this degradation-agnostic design enhances **generalization, with our method demonstrating substantial advantages in unseen and composite degradation scenario**s, as shown in **Table 4** of the main paper."}, {"review_id": "FghuN2ye3L", "reviewer": "Reviewer_kppD", "summary": "This paper proposes Latent Harmony, a two-stage framework for UHD image restoration that enhances the balance between efficiency, reconstruction fidelity, and generalization. In the first stage, a specially designed LH-VAE is trained using progressive degradation perturbation, semantic alignment via DINOv2 features, and latent space equivariance to build a robust and generalizable latent representation. In the second stage, the authors introduce high-frequency-guided low-rank adaptation which fine-tunes the encoder for fidelity and the decoder for perceptual quality using separate high-frequency losses. Extensive experiments show that the method achieves state-of-the-art performance with lower computational cost and strong generalization across various degradation types.", "questions": "1. The proposed HF-LoRA tuning strategy uses alternating optimization of encoder (FHF) and decoder (PHF) modules. This is an interesting design, but the supplementary does not report comparisons with joint tuning. It is important to know whether the alternating scheme is empirically superior, or simply a safeguard against interference. Please provide evidence or reasoning for the alternating scheme's effectiveness. For example, does joint tuning degrade latent structure or reduce performance?\n\n2. Several key components—DINOv2 for latent alignment, LoRA for efficient fine-tuning—are derived from prior work. While the integration is novel, it is important to clarify how much of the gain stems from this architecture versus the strength of the DINOv2 prior and the generality of LoRA. Have you conducted experiments with weaker backbones (e.g., vanilla ViT or ConvNeXt) or without the LoRA separation? Additional baselines in the supplementary would help isolate the contribution of the proposed synergy.\n\n3. The progressive degradation perturbation strategy is a central component of Stage 1, yet its exact scheduling policy, degradation diversity, and sensitivity to design choices remain under-explained. While the supplementary material shows some degradation types used, it is unclear how critical the progression (vs. random sampling) is to the learned latent space. Please clarify the rationale behind the PDPS schedule and whether ablation against simpler augmentation strategies (e.g., single-step or random mix) has been performed. This would help disentangle how much benefit comes from PDPS itself versus general data augmentation.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n\n* The proposed two-stage pipeline is well-structured and experimentally validated on both UHD and standard-resolution restoration tasks.\n* Ablation studies are comprehensive and demonstrate the impact of each component.\n* The results show strong quantitative performance with reduced FLOPs and parameter counts, making a compelling case for practical efficiency.\n\n**Weaknesses**:\n* Despite empirical gains, the architectural novelty is incremental—main components like VAE regularization, LoRA fine-tuning, and perceptual loss design are all based on existing paradigms.\n* The contribution may be limited by reliance on a strong pretraining backbone (DINOv2), which may obscure whether improvements stem from the framework or the visual prior itself.\n* Most components (e.g., PDPS, use of DINOv2 for semantic alignment, LoRA for tuning) are adaptations of known techniques rather than fundamental innovations.\n* The proposed HF-LoRA modules do not offer architectural novelty beyond standard LoRA tuning, and are primarily differentiated by their loss functions and placement.", "was_revised": true, "final_justification": "While this work may not introduce novel methodology, it offers a set of useful and well-supported findings regarding the application of existing techniques to UHD image restoration.", "rebuttal": "### Clarification on Motivation and Novelty\n\nWe appreciate the reviewer’s perspective regarding the use of existing techniques such as VAE, LoRA, and DINOv2. We emphasize that our core contribution lies not in inventing these individual components but in providing a novel analytical perspective and a systematic framework for the complex UHD All-in-One (UHD AIO) task through in-depth motivational analysis.\n\n**Novel Analytical Perspective:** For the UHD AIO task, which simultaneously faces **high-resolution computational bottlenecks** and **multi-degradation optimization challenges**, our work introduces a unified approach through the lens of VAE **latent space representation optimization**. Extensive observational experiments in **Fig 2** distill three core trade-offs, forming a novel analytical framework:\n1. Latent Space Level: Balancing content “generalization” with detail “reconstruction.”\n2. Optimization Level: Balancing downstream restoration task “fine-tuning adaptability” with VAE prior “structural preservation.”\n3. Output Level: Balancing decoded image “fidelity” with “perceptual quality.”\n\n**Systematic framework:**\n1. Addressing Trade-off 1: Based on the insight that “high-frequency encoding impacts generalization” (**Fig 2c**), we design a **novel regularization strategy**. The semantic alignment loss $L_{Inv}$ preserves low-frequency semantics, while the latent equivariance loss $L_{Eqv}$ balances frequency characteristics.\n2. Addressing Trade-off 2: We introduce a **high-frequency alignment loss** as a bridge for joint optimization across stages, preserving robust low-frequency semantic structures for generalization while precisely compensating suppressed high-frequency details to efficiently adapt to downstream restoration tasks, achieving an optimal balance between structural preservation and performance enhancement.\n3. Addressing Trade-off 3: We propose a separated target-decoupled LoRA, assigning the conflicting objectives of fidelity and perceptual quality to the encoder and decoder, respectively, optimized alternately. An adjustable parameter $\\alpha$ enables flexible trade-offs during inference, allowing users to tailor outputs to specific use cases.\n\nThe primary innovation of this work lies in the **comprehensive analysis** of UHD AIO task challenges presented in Fig 2 and the **problem-driven design of a systematic framework**, akin to framework-level innovations in [1-4]. Orthogonal to architectural innovations, our framework, when combined with UHDProcessor, enhances performance, as shown below, demonstrating that both framework and architectural innovations are indispensable to advancing the field.\n\n|metric|UHDprocessor|Ours|UHDprocessor+Ours|\n|-|-|-|-|\n|PSNR|29.23|29.70 |29.94|\n|SSIM|0.86|0.88|0.91|\n\n[1] RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution [ICCV 2019 oral]\n\n[2] GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity [ECCV 2024]\n\n[3] Iterative Prompt Learning for Unsupervised Backlit Image Enhancement [ICCV 2023 oral]\n\n[4] Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training [ICCV 2025]\n\n---\n\n### Q1: Comparison of Alternating Optimization vs. Joint Optimization in HF-LoRA\nWe adopt an alternating optimization scheme in HF-LoRA to mitigate gradient conflicts between competing objectives, rather than merely as a precautionary measure.\n\n**Theoretical Rationale:** As outlined in **Eq.1** of main text, our two LoRA modules pursue conflicting objectives. The FHF-LoRA (encoder), guided by a fidelity-driven loss (e.g., L1 loss), favors pixel-aligned, smooth solutions to minimize error. In contrast, the PHF-LoRA (decoder), driven by a perceptual loss (e.g., GAN loss), promotes sharper, sometimes exaggerated textures to enhance visual realism by “fooling” the discriminator. Joint optimization of both LoRA modules aligns gradients for **regressable high-frequency components**(RHF,Eq1) early in training. However, in later stages, conflicting gradients for **non-regressable high-frequency components** (NRHF,Eq1)cause training instability and hinder convergence, limiting benefits to RHF. Our alternating optimization approach mitigates these conflicts, enabling accurate restoration of RHF while allowing FHF-LoRA to smooth NRHF for improved fidelity and PHF-LoRA to generate more realistic NRHF for enhanced perceptual quality.\n\n**Supplementary Experiments:** Comparisons between alternating optimization ($\\alpha = 0.6$) and joint optimization, shown below, reveal that joint optimization yields only marginal gains over freezing the VAE in Stage 2, confirming the presence of optimization conflicts. Our alternating optimization strategy overcomes this challenge, achieving substantial improvements in both fidelity and perceptual quality.\n\n|Metric|Frozen VAE|Joint Optimization|Alternating Optimization| \n|-|-|-|-|\n| PSNR ↑  |28.48|28.62|**29.70**|\n| SSIM ↑ |0.86 |0.86| **0.877**|\n| LPIPS ↓|0.292|0.282|**0.250**|\n\n---\n\n### Q2: Disentangling Framework Contributions from DINOv2 Prior\n\nThe reviewer raises a critical point regarding the need to distinguish our framework’s contributions from those of the powerful DINOv2 prior. We fully agree and have validated this distinction in the submitted supplementary materials.\n\nComparison with Different Vision Foundation Models: In **Supp Tab 5**, we present a key ablation study comparing performance when using various vision foundation models (MAE, SAM, CLIP, SigLIP) as the backbone for our $L_{Inv}$ semantic loss. Additionally, we incorporated comparisons with the  vanilla ViT and ConvNeXt, as suggested by the reviewer. The complete results are shown below. We selected DINOv2 primarily for its established, **classic status**, yet our framework consistently improves performance across all tested backbones. This strongly demonstrates that the effectiveness of our framework stems from its **semantic alignment design**, rather than solely relying on DINOv2’s capabilities.\n\n|Method|PSNR|SSIM|\n|-|-| -|\n|VIT|29.63|0.870|\n|ConvNeXt|29.59|0.873|\n|MAE|29.62|0.872|\n|SAM|29.66| 0.880|\n|CLIP|29.57| 0.875|\n|SigLIP|29.64| 0.882 |\n| DINOv2 | 29.70 | 0.877|\n\nEffectiveness of Separated LoRA Design: LoRA serves as an efficient fine-tuning mechanism for our high-frequency alignment loss, selected for its established classic, similar to our choice of DINOv2. However, LoRA can be substituted with other parameter-efficient fine-tuning (PEFT) methods, such as Adapter, Visual Prompt Tuning, or DoRA. The results, presented below, demonstrate consistent performance improvements across these PEFT methods, indicating that our method’s gains stem not from LoRA’s efficacy but from the **high-frequency alignment loss strategy** itself. This strategy, derived from observational experiments in **Fig 2d and 2e**, enables **separated fine-tuning** for encoder fidelity and decoder perceptual quality.\n\n| Metric  |VPT | Adapter | DoRA  | LoRA(Ours) |\n|-|-|-|-|-|\n| PSNR ↑|29.66| 29.68 | 29.74 | 29.70|\n| SSIM ↑| 0.89  | 0.86 | 0.87  | 0.88|\n| LPIPS ↓| 0.256 | 0.251| 0.248 | 0.250 |\n\nThese results underscore that the **semantic alignment loss** and **efficient joint fine-tuning strategy** in our contribution are independent of specific implementations like DINOv2 or LoRA. Substituting other vision foundation models or efficient fine-tuning methods yields consistent performance improvements.\n\n---\n\n### Q3: Design Rationale and Necessity of PDPS\n\nWe thank the reviewer for their interest in the Progressive Degradation-aware Pretraining Strategy (PDPS). Below, we elucidate its design rationale.\n\n**Rationale for Progressiveness:** PDPS is grounded in **Curriculum Learning**, following a structured easy-to-hard progression. Our goal is to enhance the VAE’s semantic invariance, enabling it to map diverse degradation types and intensities to a **unified, clean semantic latent space**. As detailed in **Eq 3** of the main paper, PDPS employs probabilities $P_0 = 0.6$, $P_1 = 0.2$, and $P_2 = 0.2$, corresponding to clean images, synthetic degradation perturbations (e.g., JPEG artifacts, low-light, noise, color distortion, blur), and paired degradation image interpolation, respectively. The degradation severity $sev(t)$ and interpolation strength $\\beta(t)$ are modeled using a sigmoid-based function, $\\frac{1}{1 + e^{-\\alpha (t - \\tau)}}$ , ensuring a gradual increase in degradation intensity with training step $t$. Training with diverse and severe degradations from the outset (e.g., random mixing) risks convergence failure or learning to “ignore” input signals, hindering content-degradation disentanglement. PDPS starts with simple tasks (clean or lightly degraded images) and progressively increases degradation complexity, providing a stable learning trajectory. This ensures the VAE first establishes a robust foundation for “clean content” before addressing complex degradations.\n\n**Comparison with General Data Augmentation:** Ablation studies in **Tab 5a** of the main paper show that removing PDPS (w/o PDPS) leads to significant performance degradation. We further compare PDPS with two augmentation strategies: (1) random sampling with constant degradation intensity(RSCD) and (2) random sampling with random degradation intensity(RSRD). The results, presented below, indicate that both alternatives disrupt the curriculum learning process, forcing the VAE to learn complex degradations before establishing a stable latent space, resulting in training instability and poor convergence. In contrast, PDPS’s steadily increasing degradation intensity fosters stable training and effectively.\n\n| metric| w/o PDPS|RSCD |RSRD |PDPS|\n|:-:|:-:|:-:|:-:|:-:|\n|  PSNR  |  27.82   | 27.24 | 26.96 | 29.70|\n|  SSIM  |0.84|0.85|0.82|0.88|\n\nIn the revision, we will expand on the curriculum learning principles underlying PDPS in the supplementary materials, emphasizing that its progressive design ensures training stability and model robustness."}, {"review_id": "lqvM17V5eR", "reviewer": "Reviewer_Nq1u", "summary": "The authors propose Latent Harmony, a two-stage VAE-based framework for UHD image restoration, addressing the trade-off between computational efficiency and detail retention. In Stage One, they introduce LH-VAE, which enhances latent space robustness through semantic constraints and degradation perturbations while improving high-frequency reconstruction via latent equivariance. In Stage Two, they integrate HF-LoRA, a High-Frequency Low-Rank Adaptation module with encoder-decoder components optimized for fidelity and perceptual quality, trained via alternating gradient propagation.", "questions": "1. Although Figure 1 is presented, it is not explained in the manuscript.\n2. Figure 3 shows the entire method overview, but I can't tell what type of degradation the displayed figure shows. Furthermore, the fidelity of the restored images corresponding to different α values shows a positive correlation. Can the author explain this phenomenon?\n3. In the first stage of training, the authors propose the PDPS strategy to enhance the robustness of VAE against degradation in the latent space. Among them, p0, p1, and p2 control different perturbations, respectively, but the authors do not provide their values or reasons.\n4. Eq 4 and 5 represent two loss functions. However, for Eq 4, the author emphasizes that it is a distance metric, while Eq 5 is an L1 norm. What are the differences between the two equations?\n5. In fact, the authors adopt the VAE-based image restoration pipeline, but most of the methods compared in the experiment are based on Transform or diffusion methods. It is necessary to explain the advantages of VAE compared with these methods.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The motivation is summarized in detail and explained deeply.\n2. The experiments are rich and can prove the effectiveness of the proposed method.\nWeakness:\n1. The authors do not propose an innovative architecture, but only design multiple loss functions and training strategies.\n2. The frequent appearance of the \"xx's \" writing style in the article is not standardized.", "was_revised": true, "final_justification": "The authors have carefully addressed the issues raised in the previous round of review. They have made effective improvements to the methodology description, experimental design, and results analysis. The revised manuscript has adequately resolved my primary concerns from the initial review—the theoretical logic is now more rigorous, the data support is more robust, and the conclusions are more reliable. Based on these improvements, I recommend accepting the paper in its current form.", "rebuttal": "### Response to Comments on Architectural Innovation：\nWe appreciate the reviewer’s candid feedback. Rather than focusing on designing new architectures, this work prioritizes **problem analysis** and **framework development**. Similar to works such as Noise2Noise[1], RankSR[2], GRIDS[3],  LitCLIP[4] and TVT[5] , our contribution focuses on designing a more effective training strategy and holistic framework tailored to the task’s demands. For the UHD All-in-One (UHD AIO) restoration task, we innovatively unify the computational bottleneck of high-resolution processing and the optimization challenges of multi-degradation scenarios into a **standardized VAE latent space optimization framework**. Through extensive observational experiments, we decompose latent space representation optimization into **three core trade-offs**, leading to the development of a novel, systematic framework and training paradigm. Thus, our primary innovation lies in the **problem identification process** (i.e., the in-depth motivational analysis praised by the reviewer) and the **problem-driven framework design**. Architectural innovation in specific components is not the focus of this work but will be explored in future studies. Moreover, our framework innovation is **orthogonal** to architectural advancements. For instance, integrating our approach with the prompt-learning architecture from UHDProcessor yields further performance improvements, demonstrating that both training framework innovation and network architecture advancements are critical for UHD image restoration.\n\n\n| metric | UHDprocessor | Ours  | UHDprocessor+Ours |\n| ------ | ------------ | ----- | ----------------- |\n| PSNR   | 29.23        | 29.70 | 29.94             |\n| SSIM   | 0.86         | 0.88  | 0.91              |\n\n[1] Noise2Noise: Learning image restoration without clean data [ICML 2018]\n\n[2] RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution [ICCV 2019 oral]\n\n[3] GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity [ECCV 2024]\n\n[4] Iterative Prompt Learning for Unsupervised Backlit Image Enhancement [ICCV 2023 oral]\n\n[5] Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training [ICCV 2025]\n\n### Response to Comments on Writing Conventions\nWe sincerely thank the reviewer for pointing out the improper use of \"xx's\" in our manuscript. We acknowledge this oversight and commit to conducting a thorough review and revision of the entire manuscript in the revised version, correcting all non-standard expressions to enhance the professionalism and readability of the paper.\n\n### Q1: Explanation of Figure 1\nFig 1 provides a conceptual comparison between our framework and existing mainstream All-in-One image restoration paradigms:\n\n* Left Panel (Restoration Network): Represents traditional methods operating directly in the pixel space, which typically exhibit lower efficiency when processing UHD images.\n* Middle Panel (VAE + Degradation-aware Branch): Illustrates approaches that leverage VAEs for improved efficiency but rely on additional degradation-specific branches or prompts. This increases parameter overhead and limits generalization to unseen degradations.\n* Right Panel (Our Method): Depicts our Latent Harmony framework, which also utilizes VAEs for efficiency but enhances generalization through latent space regularization, eliminating the need for degradation-specific branches. This streamlined design achieves both higher efficiency and stronger generalization.\n\nThe figure intuitively conveys our method’s core design objectives across three dimensions—**framework efficiency**, **generalization capability**, and **output controllability**—achieving efficient, universal, and tunable restoration without relying on additional degradation-specific branches. We will include a detailed explanation of Figure 1 in the introduction of the revised manuscript to better contextualize our work. \n\n### Q2: Details of Figure 3\nDegradation Types in Figure 3:Figure 3 presents an overview featuring a noise degradation sample, with the zoomed-in image highlighting differences from the ground truth (GT). This is merely illustrative; in practice, our training encompasses multiple degradation types simultaneously.\n\nPositive Correlation Between $\\alpha$ and Fidelity: The reviewer accurately noted the positive correlation between the $\\alpha$ value and fidelity metrics, which is a deliberate aspect of our controllable inference mechanism rather than a coincidental outcome. \n\nThe $\\alpha$ parameter directly modulates the weight of the fidelity-oriented FHF-LoRA module in the encoder. This module is specifically trained with a high-frequency fidelity loss $L_{HF_{Fid}}$ to reconstruct pixel-aligned details matching the ground truth. Consequently, higher (\\alpha) values increase the contribution of the fidelity-driven module, resulting in improved PSNR/SSIM (fidelity metrics). Conversely, lower $\\alpha$ values emphasize the perceptual quality-driven decoder LoRA, trained with GAN loss, which generates visually realistic textures with slightly reduced pixel alignment. This trade-off is quantitatively supported by data in Table 5(d) of the main paper. One of our method’s key advantage lies in providing a user-adjustable parameter $\\alpha$ , **enabling flexible tuning of the trade-off between fidelity and perceptual quality based on user-specific requirements** .\n\n\n\n### Q3: Clarification on PDPS Probability Parameters\nWe thank the reviewer for this insightful question and apologize for omitting these details in the original manuscript. The specific values and their underlying rationale are as follows.\n\nIn our implementation, the probabilities for the Progressive Degradation Perturbation Strategy (PDPS) were set to $p_0=0.6$, $p_1=0.2$, and $p_2=0.2$. The rationale for this distribution is rooted in establishing a strong foundation for reconstruction fidelity while systematically introducing robust generalization capabilities:\n\n* **$p_0=0.6$ (No Perturbation)**: We assign the highest probability to the clean image ($I_{clean}$) to ensure the VAE first learns a stable and high-fidelity representation of the clean data manifold. This serves as a critical anchor, ensuring the model's primary objective remains robust reconstruction before being heavily exposed to diverse degradations.\n\n* **$p_1=0.2$ (Synthetic Degradation)**: A significant portion of the training involves applying various synthetic degradations. This step is the primary mechanism for teaching the model to generalize across a wide range of corruption types and levels in a controlled manne.\n\n* **$p_2=0.2$ (Real Degradation Interpolation)**: We also assign a probability to interpolating with a paired real degraded image ($I_{deg}$). This helps to bridge the domain gap between synthetic corruptions and complex, real-world artifacts, thereby enhancing the model's practical performance and robustness.\n\nWe will add these specific values and their detailed rationale to the implementation details in our revised supplementary material.\n\n###  Q4: Differences Between the Metric Functions in Eq. 4 and Eq. 5\nWhile both Equations 4 and 5 employ distance metrics, they operate in distinct spaces and serve entirely different objectives.\n\n**Eq. 4:**  $L_{Inv} = d(z_{deg}^{\\prime}, f_{VFM})$ . This loss function operates in a high-dimensional semantic feature space, computing the distance between the VAE’s latent encoding $z_{deg}^{\\prime}$ and features $f_{VFM}$ extracted by a robust pretrained vision model (DINOv2). Its objective is to ensure semantic consistency, preserving the core semantic representation in the latent space regardless of image degradation. We adopt a cosine similarity-based distance metric $d(\\cdot,\\cdot)$, as the direction of feature vectors is more critical than their absolute magnitude in high-dimensional feature spaces.\n\n**Eq. 5:**  $L_{Eqv} = |D_{\\psi}(z_{down}) - I_{down}|$ .This loss function operates directly in the image pixel space, comparing the decoded image from a downsampled latent encoding $D_{\\psi}(z_{down})$ with the directly downsampled original image $I_{down}$. Its goal is to ensure structural equivariance across scales. We use the L1 norm due to its robustness to outliers and its established effectiveness as a standard loss in image reconstruction, compared to the L2 norm.\n\n### Q5: Comparison of VAE with Transformer/Diffusion Methods\n\nTo clarify, our method is not a standalone VAE framework positioned against Transformer or Diffusion models. Instead, it proposes a **“latent space restoration framework”** tailored to UHD image characteristics, in contrast to **traditional “pixel space restoration frameworks”** . This framework’s key advantage lies in shifting computationally intensive restoration tasks from dense pixel space to a compact latent space, significantly improving efficiency, as evidenced in **Table 5c**. Importantly, our approach is **orthogonal** to Transformer and Diffusion methods, as demonstrated by results with Restormer and SFHformer in **Table 5c** and the LDM-based Diff-plugin in **Table 3**. The proposed Latent Harmony VAE framework substantially enhances the **computational efficiency** and **restoration performance** of Transformer and Diffusion methods."}]

Author rebuttal:

Meta-review: The paper presents a robust and well-thought-out framework that tackles a challenging problem in image restoration. The authors' systematic approach to identifying and solving trade-offs within the VAE latent space, combined with their effective rebuttal, has convinced the reviewers of the paper's merit. The AC-panel concurs with their decision and recommends acceptance.
Decision: Accept (poster)