Seeing Through the PRISM: Compound & Controllable Restoration of Scientific Images
Authors: Rupa Kurinchi-Vendhan
Pratyusha Sharma
Antonio Torralba
Sara Beery
Venue: ICLR 2026 Poster (2026)
Keywords: prompted diffusion
image restoration
expert-in-the-loop
scientific imaging
Primary area: generative models

Abstract: Scientific and environmental imagery often suffer from complex mixtures of noise related to the sensor and the environment. Existing restoration methods typically remove one degradation at a time, leading to cascading artifacts, overcorrection, or loss of meaningful signal. In scientific applications, restoration must be able to simultaneously handle compound degradations while allowing experts to selectively remove subsets of distortions without erasing important features. To address these challenges, we present PRISM (Precision Restoration with Interpretable Separation of Mixtures). PRISM is a prompted conditional diffusion framework which combines compound-aware supervision over mixed degradations with a weighted contrastive disentanglement objective that aligns primitives and their mixtures in the latent space. This compositional geometry enables high-fidelity joint removal of overlapping distortions while also allowing flexible, targeted fixes through natural language prompts. Across microscopy, wildlife monitoring, remote sensing, and urban weather datasets, PRISM outperforms state-of-the-art baselines on complex compound degradations, including zero-shot mixtures not seen during training. Importantly, we show that selective restoration significantly improves downstream scientific accuracy in several domains over standard ``black-box'' restoration. These results establish PRISM as a generalizable and controllable framework for high-fidelity restoration in domains where scientific utility is a priority.

Published as a conference paper at ICLR 2026
SEEING THROUGH THE PRISM: COMPOUND & CONTROLLABLE RESTORATION OF SCIENTIFIC IMAGES
Rupa Kurinchi- Venncha,, Pratyusha Sharma, Antonio Torralba, Sara Beery Massachusetts Institute Technology
ABSTRACT
Scientific and environmental imagery often suffer from complex mixtures of noise related to the sensor and the environment. Existing restoration methods typically remove one degradation
time, leading to cascading artifacts, overcorrection, or loss of meaningful signal. In scientific applications, restoration must be able to simultaneously handle compound degradations while allowing experts selectively remove subsets of distortions without erasing important features. To address these challenges, we present PRISM (Precision Restoration with Interpretable Separation of Mixtures). PRISM is prompted conditional diffusion framework which combines compound-awar supervision over mixed degradations with a weighted contrastive disentanglement objective that aligns primitives and their mixtures in the latent space. This compositional geometry enables high-fidelity joint removal of overlapping distortions while also allowing flexible, targeted fixes through natural language prompts. Across microscopy, wildlife monitoring, remote sensing, and urban weather datasets, PRISM outperforms state-of-the-ard baselines on complex compound degradations, including zero-shot mixtures not seen during training. Importantly, we show that selective restoration significantly improves downstream scientific accuracy in several domains over standard "black- box" restoration. These results establish PRISM framework for high-fidelity restoration in domains where scientific utility is a priority. Our code and dataset are available at this link.
generalizable and controllable
Degradation
Prompting
"Denoice this, and incresse
"Theme's bacioria between these colls!
al is deer cating? Increase the brightness."
"Creel, It's eeting maple laaves:
*Can you rovrove the contrast?
QR
Handling Unseen Composites
Biighhnoss.
docid.ous forest"
Haze a Colar Shill.
"Brighton rt and
"Now, we can estimate dwersty
User-Driven
Automated
contract.
Figure 1: Expert-in in-the- Loop Restoration with PRISM. PRISM enables robust compound restoration and zero-shot handling of unseen mixtures. It supports both automatic restoration and prompt-driven, selective correction for scientific analysis.
"Corresponding author: rupak @mit.edu

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INTRODUCTION
Scientific and environmental imagery is rarely affected by single degradation. Instead, images lypically suffer from compounding effects that vary across datasets and collection settings: underwater images combine low light, scattering, and wavelength-dependeen absorption effects (Akkaynak & Treibitz, 2018; Chiang & Chen, 201 1), while satellite imagery suffers from overlapping sensor noise, haze, and cloud occlusions (King et al., 2013; Ahmad al., 2019).
Specialized single-distortion models (c.g., for dehazing or removing clouds) enable domain experts to preprocess noisy data before conducting analysis. These approaches are often carefully hand-crafted for specific datasets and distortion types, making them brittle when degradations occur unpredictably, especially in scientific settings where ground truth is unavailable and the underlying distortions are not known a priori. For example, camera trap data may be affected by combinations of motion blur, weather, and lighting that can vary across images from the same deployment, making single- distortion pipelines ineffective. While this has motivated generalist "all-in-one" models (Li et al., 2020; Potlapalli al., 2023b), current frameworks perform sequential/ieeratie removal of single distortions, which may lead to cascading artifacts and indiscriminately remove errors, erasing signals that should be preserved.
In scientific applications, restoration must preserve signals critical for precision and analysis, not just aestheties, Aggressive restoration can inadvertently introduce more error: denoising may erase faint galaxies in astronomy data (Starck et al., 2002), and super-resolution can hallucinate subcellular structures in microscopy images (Christensen, 2022). Few models let experts control these tradcoffs.
We argue that restoration in scientific contexts requires three principles: simultaneous over sequential correction, precision over aesthetics, and in some cases, control over automation. We introduce PRISM, conditional diffusion framework that simultaneously disentangles compound degradations and enables expert-guided, precision restoration. Figure shows example use-cases of PRISM across scientific domains, demonstrating how our approach enables robust and interactive mixed degradation removal, how automated restoration can infer distortion types from an input image, and how expert prompts can selectively target distortions while preserving critical features. Our contributions are:
1. A principled embedding design for compound degradations, combining weighted con- trastive learning with compound-awan supervision. The resulting structured, composi- tional latent space yields separable embeddings for primitives and their mixtures, enabling both automated compound restoration and selective -istortion-specific control;
2. A novel benchmark for scientific utility spanning remote sensing, ecology, biomedical, and urban domains- Domains-includiig -including our reflects the needs of scientific workflows by evaluating task fidelity rather than perceptual quality;
Rooftop Cityscapes dataset that
3. A systematic study showing that controllability is not convenience but a necessity. Selective, distortion-speeifie guidance significantly improves downstream scientific accuracy under compound degradations.
RELATED WORKS
2.1 RESTORATION IN SCIENTIFIC DOMAINS
Restoration has long been important for scientific imaging: early astronomy corrected photographic plates (Gull& & Daniell, 1978), while biomedical imaging relied on denoising and deblurring for diagnostics (Buades al., 2005; Dabov al., 2007). Modern deep learning pipelines destripe astronomical surveys (Liu et al.. 2025; Vojtekova et al., 2021) and denoise MRI images (Yan et al., 2024; Manjón & Coupe, 2018; Kidoh et al., 2020). While effective within their target domains, these methods typically assume that degradations are fixed, known, and well represented at training time.
In practice, scientific images often exhibit compound degradations. Domain-speciffe models such as Sea-Thru (Akkaynak & Treibitz, 2019) for underwater image correction explicitly model these effects, but rely on tailored, paired datasets which can limit their generalization when conditions deviate beyond their assumptions.
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Additionally, cleaner data is not always better: scientific restoration often requires targeted improve- ments rather than blanket restoration. In microscopy, Lu et al. (2025) show that over-denoising can erase weak but biologically meaningful structures; in underwater monitoring, Cecilia & Murugan (2022) find that generic denoisers oversmooth fine-scale marine features critical for ecological inter- pretation. These findings motivate restoration frameworks that handle compound effects while allowing experts to control which degradations are addressed.
2.2 HANDLING COMPOUND DEGRADATIONS
To address compound corruptions, several works propose seaaredbbacbbong or "all-in-one" architec- tures that jointly train on multiple degradation types, including All- Ell-eettherNet (Li et al., 2020), TransRestorer (Chen et al., 2021), and SmartAssign (Wang et al., 2023). More recent universal models such as MT-Restore (Chen et al., 2022b), All-in-OneNet (Li et al., 2022b), and PatchDif- fuser (Özdenizei & Legenstein, 2023) improve flexibility by expanding training coverage across degradation categories. These approaches use single-distortion removal for compositional restoration which may introduce unwanted artifacts propagating errors from one stage to the next. Composite approaches, including OneRestore (Guo et al., 2024) and AllRestorer (Mao et al., 2024), explicitly model interactions between degradations rather than treating them independently, leading to im- proved performance on benchmark mixtures seen during training. Although these methods improve robustness mixtures observed during training, they do not explicitly enforce a compositional latent structure that guarantees predictable behavior when selectively conditioning on subsets of degradations.
Research in disentangled representation learning suggests that factorized latent structures significantly improve generalization novel combinations of known components (Burgess al., 2018; Lake et al., 2017), particularly when architectures are designed to maintain the independence of these latent features as they transition through successive layers (Liang et al., 2025; Träuble et al., 2021). Our approach builds this theory by aligning degradation mixtures with their primitives in the latent space to enable generalization to unseen data.
2.3 CONDITIONAL DIFFUSION AND PROMPT-GUIDED RESTORATION
Closely related methods on blind image restoration (BIR) handle cases where degradation types and severities are unknown at test time. Early GaN-based approaches such as BSRGAN (Zhang et al., 2021) and Real-ESRGAN (Wang et al., 2021) simulate diverse degradation pipelines to improve robustness under real-world conditions. Diffusion-based restoration more broadly extends conditional modeling with higher fidelity and stochastic control (Ho et al., 2020; Dhariwal & Nichol, 2021; Rombach et al., 2022). More recent diffusion-basee BIR methods, including StableSR (Nagar et al., 2023) and DIfIBIR (Lin et al., 2024), leverage pretrained diffusion priors generate realistic and high-fidelity restorations without requiring explicit degradation labels. These models demonstrate strong perceptual quality and robustness to complex degradations. However, they rely learned image priors and do not explicitly use semantic information from vision-language representations.
Prompt-guided approaches offer more flexible interface for controlling restoration behavior. Meth- ods such as PromptiR (Potlapalli et al., 2023b), demonstrate that conditioning on auxiliary text improves all-in-one restoration across fixed vocabulary of degradation categories. DiffPlugin (Liu et al., 2024), MPerceiver (Ai et al., 2024), and AutoDIR (Jiang et al., 2024) introduce text- and image-conditioned diffusion models for universal image restoration. However, in these approaches, degradations are not explicitly disentangled in the model's latent space. As result, removing one distortion may modify the image in unexpected ways, limiting reliable partial restoration. We distinguish between prompt-conditiond restoration and structurally controllable restoration: the former allows specifying tasks, whereas the latter requires a representation in which degradation factors are disentangled enough support predictable, selective intervention.
In parallel, several works adapted langun-e models for restoration by fine-tuning CLIP representations to be degradation-aware. DA-CLIP aligns image embeddings with textual degradation descriptions, improving robustness and downstream restoration performance under domain shift (Luo et al., 2023; 2024). AutoDIR further introduces cemantic-agnostie loss that encourages CLIP to distinguish clean from degraded images based on quality-relatee cues rather than semantic content, biasing the encoder toward degradation-ssnsstive features (Jiang et al., 2024). These works primarily
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focus on aligning representations to individual distortion types rather than enforcing compositional structure that supports systematic generalization to unseen mixtures.
A recent survey by Jiang et al. (2025) highlights limited support for real-world compound degradations and appropriate evaluation protocols. Existing methods also lack compositional structure for stable joint correction and distortion-specine control. We address these gaps by modeling degradations compositionally and introducing a mixed degradations benchmark and a downstream scientific task evaluation, which together assess structural generalization and practical utility under realistic compound settings.
METHODS
PRISM combines compound-aware supervision with contrastive latent disentanglement to enable robust and controllable restoration under mixed degradations.
3.1 BUILDING A DATASET OF COMPOUND DEGRADATIONS
We construct synthetic dataset from diverse scientific domains: ImageNet (Deng et al., 2009), Sentinel-2 patches from IMS (Schmitt et al., 2019), iWildCam 2022 (Beuving et al., 2022), EUVP underwater imagery (Islam et al., 2020). CityScapes (Cordts al., 2016), BioSR microscopy slides (Gong et al., 2021), Brain Tumor MRI (Nickparvar, 2021), and high-resolution Subaru/HSC sky surveys (Miao et al., 2024). Across these datasets, we sample 2M clean images that serve as the ground truth targets during training. See Appendix B for more details.
Compound-Awan Supervision. Each image is degraded by up to three distortions sampled from a library including geometric warping, blur, photometric shifts and weather effects, etc. We apply maximum of three distortions per image to capture challenging compound cases while maintaining enough of the original semantic content (see Table 9 in Appendix E). Each image is distorted by composition of multiple augmentations.
Following prior work such -ERAAN (Wang et al., 2021). which demonstrate that increased variability in training degradations can improve robustness and generalization, we construct a diverse spectrum of degradations better approximate real-world conditions (Luo et al., 2024; 2022; Zhang et al., 2021). The distortions are applied in random order with varied parameters, such as kernel size for blurring or angle of snowfall, that determine degradation intensity. We GPT-4 (Hurst et al., 2024) to generate variable natural language prompts describing image degradations to simulate realistic user instructions (e.g., "remove haze," "dehaze this image." "dehaze and super-resolve this"). We also include partial prompts (remove a subset of distortions) and negative prompts (remove non- non-present distortion). This design is critical for selective restoration: by exposing the model to submixtures and negative conditions, we encourage it to associate each degradation primitive with distinct latent direction and to avoid unintended corrections when a distortion not specified. This supervision explicitly supports predictable, distortion-specific intervention at inference time. Overall, the dataset consists triplets (Lelcan Ldist p). Further details on dataset sampling and the distortion pipeline are provided in Appendix B.
3.2 THE PRISM MODEL
Our framework builds on composite/all-in-on restoration (Guo et al., 2024; Jiang et al., 2024; Ai al., 2024) but emphasizes controllability and precision under compound degradations. Figure outlines our process: we first fine-tune the CLIP image encoder on our mixed degradation dataset to ensure that embeddings preserve semantic content while becoming distortion-invariinn, keeping the text encoder frozen. Once adapted, we freeze both CLIP encoders to provide a stable conditioning space for training the latent diffusion backbone.
Stage 1: Disentangling Distortions. Naive CLIP embeddings are poorly suited for restoration, as they primarily cluster images by semantic content rather than degradation type or quality (Radford et al., 2021). Prior work (Jiang al., 2024) showed that quality-aware embeddings improve performance by shifting focus from semantics to distortions. We extend this to compound degradations by explicitly modeling compositionality, ensuring embeddings reflect the presence and overlap of
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Fine-Tune Image Encoder
Diffusion- Basdd Restoration
Degraded Input
All Other Mixtures
magge
LQ Output
Final Output
Oerage
Diflusion Model
Haze + Low Light
SCPM
Haze
Low Light
"seenaze and brighten this"
Clean
Prompt
Figure 2: Overview of PRISM. We first fine-tune CLIP's image encoder disentangle image embed- dings by distortion. The degraded input and user prompt are then used to condition the latent diffusion backbone, the coarse outputs of which are refined with a Semantic Content Preservation Module (SCPM) to yield the final restored output. Appendix Figure 12 shows the SCPM's 's architecture.
multiple distortions (c.g., an image degraded by haze+rain should be more similar to haze-only and rain-only images than images degraded by unrelated distortions such noise).
Let Telean be a clean image and (1 } its m degraded variants. For cosine similarity sim(; we use contrastive loss aligning the corresponding embeddings dist with Eçleun and repelling from its 1 sibling variants and all variants from other images in the batch. To encode the similarity structure different combinations of distortions, we use the Jaccard distance (Jaceard, 1901) between their distortion sets d() and (e)
Wyk = exp 1
(d6) n n (dC) U d(t) ))
We analyze this weighting mechanism in Appendix E. The resulting per- per-variant loss
exp(sim(e C/lean)TT)
C
log
(3) Σxes Wikk exp(sim(e disu (16) ( ElEBurer cxp(sim ()
othher TT)
256 clean images per batch, each with Bother 256 randomly sampled degraded variants from our library of primitive and compound distortions.
for r 0.10. We
The contrastive objective encourages degraded embeddings reflect distortion structure, but it does not explicitly prevent the clean embedding from drifting toward introduce aware regularizer that penalizes the clean embedding for exhibiting distortion evidence
features. We
Σ $(c Gelean):
quall
cEd())
where $(c (etean) is the predicted probability of distortion e from 6elean This term discourages degradation hallucination and anchors the representation to remain distortion-fret free for clean inputs.
The final encoder loss is LCUP E ( yielding embeddings that: (i) align degraded and clean images, (ii) reflect compositional overlap, and (iii) support controllable restoration.
Stage 2: Restoring with Conditional Diffusion. We adopt a latent diffusion model, Stable Diffusion v1.5 (Rombach et al., 2022). During training, we replace the standard CLIP image encoder with our fine-tuned compound distortion-awe aware encoder, providing semantic and degradation context. Then, we concatenate the conditioning vector from the image encoder with the text embedding of the restoration prompt from the frozen CLIP text encoder. These embeddings are passed through a learnable cross- attention layer at each UNet block following the original Stable Diffusion v1.5 design for prompt conditioning. This allows PRISM to attend to the desired set of degradations during each denoising step. Unlike sequential approaches (Jiang et al., 2024), PRISM conditions the full composite prompt in a single denoising trajectory. enabling joint restoration of overlapping artifacts. While MPerceiver (Ai et al., 2024) encodes multiple degradations concatenated tokens, this strategy
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represents
inputs but does not explicitly enforce disentangled compositional structure in the embedding space that supports controlled recombination.
Latent diffusion blur fine-scale structures critical in scientific imagery. Following Jiang et al. (2024), we integrate a Semantic Content Preservation Module (SCPM), a lightweight decoder-side refinement block that adaptively fuses encoder and decoder features to preserve edges and small textures. Full architectural details and ablations are provided Appendix E.
For fair comparison, all baselines trained on the fixed set of primitive distortions. Training details and compute requirements are provided in Appendix A, baselines are described in Appendix D, and ablations over model components provided in Appendix E.
3.3 PROMPTING
PRISM enables both expert-guided and automated restoration by grounding free-form natural lan- guage prompts structured task space.
At inference time, PRISM supports:
Expert-guided restoration: A free-form instruction (e.g., "remove fog and color shift") is embedded with the frozen CLIP text encoder and used to condition diffusion.
Automated restoration: Given input image, a lightweight MLP predicts multi-label distortion set from the image embedding. This set converted into a standardized prompt ("remove distortions I. y, 2") and encoded by CLIP.
In both prompting scenarios, the text embeddings condition the latent diffusion model through cross-attention at each denoising step. Sensitivity to prompt variation analyzed in Appendix E.
3.4 EVALUATION
We evaluate PRISM on: (1) compound and controllable restoration, (2) handling unseen real-worke composites, and (3) downstream utility. Unless noted otherwise, we evaluate manual restoration with predefined distortion types, using the free-form prompts generated by GPT-4. Full details on datasets and evaluation are in Appendix B and C.
Mixed Degradations Benchmark (MDB). We use the held-out subset of the triplets (Jeleun: Jaias p) from our dataset as our fixed testbed. This MDB measures sequential vs. composite prompting and prompt faithfulness under compound degradations. This dataset extends beyond the CDD-11 dataset proposed by Guo et al. (2024) to span broad set of real-world degradations, with varied intensities.
Handling Unseen Distortions. For zero-shot tests, we evaluate on real domains with compound distortions not explicitly seen in training: underwater effects in UIEB (Li al., 2019), under-display camera artifacts (Zhou et al., 2021), and fluid distortions (Thapa et al., 2020). These datasets probe PRISM's ability extend to novel distortions.
Downstream Utility. Standard benchmarks measure pixel similarity to a clean reference, but such metrics fail to capture whether restored images remain scientifically useful. We instead evaluate restoration through downstream tasks using real datasets with natural distortions and undistorted views as ground truth. To reflect how restoration outputs are typically used in practice, we use off-the-she pretrained models, giving conservative but practical measure of utility. We test across four domains:
1. Remote sensing with Sen12MS (Schmitt et al., 2019):
landcover classification (Papoutsis et al., 2023) on cloudy satellite data, with labels from cloudless samples.
2. Wildlife monitoring with iWildCam 2022 (Beuving et al., 2022): species classification with SpeciesNet (Gadot et al., 2024) on low-confidence nighttime images with expert labels from cigh-confidence frames of the same sequence.
3. Segmentation and fluorescence in microscopy with BioSR (Gong et al., 2021): segmenta- tion and fluorescence/ineenstty measurements of clathrin-coated pits from low signal-to-noise

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data using MicroSAM (Archit et al., 2025), compared to high quality structured illumination microscopy (SIM) ground truth.
4. Urban forest monitoring using our novel Rooftop Cityscapes dataset: panoptic segmen- tation (Lin et al., 2017) of cityscapes under haze/low light, with paired, labeled clear-weather data. See Appendix for details on this custom dataset.
RESULTS AND DISCUSSION
4.1 BREAKING THE CASCADE: COMPOUND RESTORATION MADE ROBUST
Table I: PRISM outperforms baselines with manual prompting on MDB, where each test image has up to three distortions. Best results bolded, second-best underlined.
Sequentially removing distortions often accumulates errors, artifacts, or incon- sistencies, while restoring all distortions jointly yields more stable, high-fidelity results. Our MDB evaluation supports this intuition, with qualitative results in Appendix Figs. 17 and 16.
Category
Method
PSNR
SSIM f
FID
LPIPS
AirNet
0.742
0.382
Restormera
16.32
0.768
70.11
0.365
All-in One
Table highlights a divide between early all-in-one models (Li et al., 2022a; Za- mir et al., 2022: Chen et al., 2022a; Potla- palli al., 2023a), which are trained per distortion and generalize poorly to mix- tures, recent diffusion approaches Liu et al. (2024): Ai al. (2024); Jiang et al. (2024), and models that explicitly target composite restoration (Guo et al., 2024). While OneRestore trained composite datasets like PRISM, it fails to match the perceptual quality of diffusion model outputs; however, these diffusion methods typically remain limited by their reliance on single-distortion or sequential training regimes.
NAFNet
0.776
PromptIR
18.11
0.801
62.78
0.298
DiffPlugin
0.821
53.88
0.255
Diflusion
MPPecceivr
20.84
0.829
48.18
0.235
50.75
0.246
Composite
OneRestore
19.36
0.812
59.42
0.276
PRISM (ours)
22.08
0,842
48.97
0.218
PRISM achieves the best results across both fidelity (PSNR/SSIM) and perceptual metrics (FID/LPIPS), owing two design choices: (1) compound-aware supervision over mixed degradations, and (2) contrastive disentanglement of distortions. We study the contributions of each below.
Compound-aware supervision supports restoration under increasingly complex mix- tures. Training on combinatorial mixtures of degradations (full, partial, and negative restoration) allows simultaneous removal of multiple effects without cascading errors. Fig. 3 shows that while PRISM matches baselines on images with a single degradation, it significantly out- performs them as complexity grows, especially on unseen cases with four distortions (an extension of the MDB set). Training on composites explic- itly outperforms training primitives separately, and that improved image embeddings from our contrastive loss provide an additional boost over base- lines. Importantly, this compound- aware structure not only improves ro- bustness under increasing distortion complexity, but also lays the founda- tion for selective restoration. These trends hold consistently across other metrics (SSIM, LPIPS, and FID), as shown in Appendix Fig. 14).
PSNR
25-
11112
A 11.33
À 10.56
8.14
20-
No. Distortions
15-
10-
AncDR
MPtrooiver
Primiive Aware)
PRIS
PRSM (Compound Aware)
Methods
Figure 3: PRISM trained on composite examples scales best with the number of distortions. This outperforms our model trained on each degradation separately as well comparable baselines, emphasized by the A PSNR of test images with 4 distortions) above each bar.
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PSNR (1)
Contrastive disentanglement improves compositional restoration. Appendix Fig. 13 visualizes the effect of the fine-tuning CLIP on the image embedding space. Our compound-aware weighted contrastive loss explicitly in- corporates mixed degradations, enforcing latent geom- etry that pulls compound distortions toward the span of their primitives (right). This design closes the gap be- tween sequential and single-shot prompting (Fig. 4), en- suring that multi-step restoration behaves predictably and that single-shot prompts achieve comparable fidelity. Ap- pendix Fig. 15 demonstrates that these improvements gen- eralize beyond PSNR. This allows experts to target spe- cific degradations without disturbing other content. By combining compound- aware supervision with contrastive disentanglement, PRISM not only outperforms existing baselines restoring compound degradations, but also maintains fine control.
23
Settings
22
Composite
20
17
Compaund-AAarr CPIP Figure 4: Latent disentanglement en- ables both stepwise and single-shot restoration, closing the gap between prompting strategies.
Photrainud CLIP
Phirilie-Awan
CLI
4.2
COMPOSITIONALITY ENABLES ADAPTIVE RESTORATION IN NOVEL SETTINGS
The compositional structure of the image latent space also supports generalization unseen degra- dations. degradations are represented compositionally, then novel composites can be modeled as combinations of known primitives. This means PRISM can automatically identify constituent distortions and restore them, even if the exact combination was never seen in training.
We evaluate zero-shot restoration on three domains (underwater imagery, under-display cameras, and fluid lensing), each containing complex. previously unseen distortions (Table 2). For each dataset, we use the compound-aware CLIP encoder to identify the fixed of distortion types present in the images of each dataset. We then apply the same manual prompts over this standardized set for all models to ensure a fair, consistent evaluation. While the predicted distortion categories for UIEB were more variable and often reflected mixtures of multiple effects such as low light, haze, contrast, and color shifts, the POLED and ThapaSet datasets exhibited more uniform and stable classifications, providing clearer mappings to their dominant distortion types: low light, blur, and contrast vs. refraction and warping, respectively. Qualitative examples are shown in Appendix F.
Table 2: PRISM achieves state-of-the- zero-shot performance across underwater (UIEB), under- display camera (POLED), and fluid lensing (ThapaSet) benchmarks. Best results are bolded, second- best are underlined.
Category
Method
UTEB
POLED
ThapaSet
PSNR f
SSIM f
LPIPS
PSNR
SSIM 1
LPTPS
SSIM
LPIPS
AirNet
17.51
0.768
0.468
13.61
0.582
0.529
18.41
0.388
0.609
Restormer,
18.13
0.792
0.454
14.38
0.608
0.512
19.31
0.413
0,588
All-in-One
NAFNeiA
17.76
0.756
0.479
13.02
0.591
0.543
18.94
0.401
0.596
PrompeIR
19.76
0.858
16.42
0.651
0.564
DiffPlugin
20.72
0.874
0.392
17.01
0.659
0.451
21.15
0.454
0,536
Diffusion
MPerceiver
28.18
0.889
0.366
17.55
0.669
0.436
21.41
0.459
0.522
AutoDRR
21.02
01887
0.374
17.33
0.665
0.431
21.53
0.462
0,528
Composite
OneRestore
20.53
0.869
0.404
16.82
0.654
0.457
20.82
0.446
0.548
PRISM (ours)
22.18
0.914
0.331
18.26
0.694
0.419
22.36
0.487
0.492
Both all-in-one models like AirNet and Restormer and composite methods such as OneRestore fall short in capturing complex degradation interactions without robust priors. In contrast, diffusion models like AutoDIR generalize well but often treat distortions isolation, which may introduce unwanted intermediate errors in linearly restoring inputs.
PRISM's state-of-the-art zero-shot performance is driven by a latent space that treats compound degradations as structured combinations of primitives. Rather than attempting model the complex, non-linear physics of every possible distortion interaction, the contrastive design enforces compo- sitional logic. By training submixtures, the model learns to represent composite (e.g., haze + overexposure) as joint embedding of constituent parts. This allows the model to map previously unseen mixtures into known coordinate system defined by established primitives, effectively "in-
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terpolating" restoration strategy for novel compounds. Consequently, PRISM avoids the pitfalls of brittle category memorization, enabling the restoration of unseen mixtures using the semantic proximity between known primitives and unknown composites without additional supervision.
Input
Automated Output
Expert-Driven Outputs
Dronc Image of Reof
Warp Color
"unwarr""
"ix coloring"
"unbiur
Figure 5: Structured, compositional latent geometry supports both automated (left) and expert-driven (right) generalization. With an expert-in-the-logy prompts progressively target distortions.
Fig. 5 illustrates how this design translates into practice with high-resolution drone imagery over coral reefs in Mo' orea (Saccomanno et al., 2025). While our method enables automated classification and correction of novel distortions, also provides interpretability and control: experts can explore how distortions relate, refine restoration strategies, and avoid black-box corrections. In improving performance on partial restoration, PRISM enables experts to issue stepwise prompts (c.g.. "unwarp," "fix coloring," "unblur") iteratively restore unseen distortions they see fit.
4.2.1 THE CASE FOR CONTROLLABILITY IN SCIENCE
While PRISM restores images robustly under compound degradations, we argue that some appli- cations, restoration quality cannot be judged by appearance alone. We assess PRISM's impact downstream tasks across four domains: remote sensing, ecology. microscopy, and urban monitoring.
This raises critical question: if a model can remove all degradations, should it? In scientific imaging, often no. Distortions can be mixed with faint but meaningful signals, and indiscriminate restoration may erase these cues introduce artifacts that mislead downstream models. Maintaining downstream fidelity requires control: experts must choose what to correct versus preserve.
As shown in Table 3, controllability significantly improves downstream performance over full restoration (automatically detecting all distortions present) in three of four domains. For instance, in nighttime camera trap data, restoring only contrast may improve recognition over full restoration, which can blur subtle texture cues. In urban scenes, removing haze improves segmentation, but also brightening the image may over-adjust vegetation in the distance. Remote sensing is the exception: full, automatic restoration performs slightly better, removing only clouds leaves images inder-illuminated and hazy.
Table 3: Selective controllability outperforms full restoration across three of four downstream tasks. We report mean + std over 3 random seeds. Best results are bolded.
Domain
Degraded Input
Full Restoration
Selective Restoration
p-value
Remote sensing (Avc. 1)
0,781 0.013
0,842 : 0.011
0,836 1 0.012
0.11 (n.s.)
Camera Traps (Ace. 1)
0.921 1 0.004
0.976 1 0.008
0.984 1 0.004
0.032 0.05
Microscopy (mloU
0.353 0.015
0.475 - 0.012
0,580 : 0.010
0.018 < 0.05
Urban scenes (mloU f)
0,548 +: 0.018
0.615 = 0.014
0.650 = 0.012
0.041 < 0.05
Fig. 6 illustrates use case of controllability in microscopy: super-resolution alone improves segmentation alignment with SIM ground truth, but additional denoising erases faint, biologically relevant structures. Additional examples across other domains are included in Appendix F.
In some cases, controllability enables different analyses on the same data which may depend on fundamentally different visual cues. Within microscopy, segmentation of clathrin-coated pits relies on high-frequency structural detail, such as sharp boundaries and local contrast that delineate subcellular objects. In contrast, fluorescence measures the mean pixel intensity within biological regions, proxy for molecular concentration or protein recruitment. This calculation is highly sensitive to intensity bias and noise variance but relatively tolerant to mild blur.

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Published as a conference paper at ICLR 2026
LQ Microscopy Sensor
Compress Noise
"super-reeowve"
HQ Microscopy Sensor
0.30
mIOU: 0.496
miOU: 0.569
Figure 6: Selective restoration improves segmentation of clathrin-coated pits in microscopy. Super- resolution alone improves mloU, while automatically detecting and removing noise suppresses faint but biologically relevant signals (see regions encircled in red), reducing accuracy.
Table 4 summarizes performance across restoration settings. Super-resolutin yields the best seg- mentation mloU by enhancing edges and subcellular boundaries, but this same sharpening increases fluorescence error. Denoising has the opposite effect. While preserves the intensity distribution of input data, producing the lowest fluorescence MSE, it removes fine structure needed for accurate segmentation. Combined restoration produces intermediate degraded results, demonstrating that no single transformation can satisfy both scientific objectives simultancously.
These findings highlight that restoration is task- dependent, and experts may not always know how preprocess their data beforehand. Blanket restora- tion can overlook the varying demands of scientific workflows. PRISM's s controllability enables experts to ensure that restored images remain faithful to the underlying scientific signal as needed.
Table 4: Task-dependent restoration per- formance on BioSR. Segmentation prefers structural enhancement, whereas fluores- cence quantification requires accurate inten- sity preservation. No single restoration setting is optimal for both tasks.
Restoration Prompt
Segmentation miloi
Fluoreseence MSE 1
Important challenges remain for PRISM. Our training still depends on synthetic augmentations that cannot fully capture real distortions. Moreover, extending controllability beyond specifying which distortions remove their intensity and spatial extent would enable localized restoration and finer-grained preservation scientific signals.
Denoise
0.479
0.006
Super Resolution Combined No restoration
0.580
0.018
0.475
0.014
0.356
0.025
While diffusion-baan based restoration typically demands greater computational resources than traditional encoder-decoder architectures, PRISM maintains competitive runtimes. As detailed in Appendix (Table 13), its latency is comparable to existing state-of-the-ard diffusion frameworks. In this way, PRISM balances practical efficiency with addressing key priorities in scientific restoration.
CONCLUSIONS
Our results show that controllable and compound-aware restoration is critical for scientific and environmental imaging. PRISM outperforms both specialized and generalist baselines by combining (I) compound-awarr supervision, which exposes the model overlapping degradations, and (2) weighted contrastive disentanglement, which organizes the latent space according to composite distortions. Together, these yield more robust and interpretable restoration.
We also find strong generalization beyond curated training sets. PRISM achieves robust zero-shot performance on underwater imaging, under-display camera correction, and fluid lensing, showing that compositional representations extend naturally unseen domains. Importantly, evaluations on real composite degradations confirm generalization beyond our synthetic training pipeline.
A key insight is that more restoration is not always better. Across diverse domains, we show that indiscriminate removal degradations suppresses faint but meaningful signals or introduces artifacts. Moreover, the appropriate level and type of restoration analyses may rely on distinct visual cues and therefore benefit from different restoration strategies. Allowing experts choose which distortions to correct is essential for scientific precision.
be task-dependent,
different scientific
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported in part by a Schmidt Science's A12050 Early Career Fellowship, NSF CAREER Grant (Award No. 2441060), an NSF Graduate Research Fellowship (Award No. DGE- 2141064), the NSF and NSERC AI and Biodiversity Change Global Center (NSF Award No. 2330423 and NSERC Award No. 585136), and the MIT Generative AI Consortium.
ETHICS STATEMENT
This work relies exclusively on publicly available datasets and synthetic distortions; all source data cited appropriately. We acknowledge that restoration models be misapplied or introduce misleading artifacts, and note that our system is not intended for high-stakes decision-making without domain expert oversight, but overall framework design for handling complex real-world data.
REPRODUCIBILITY STATEMENT
Our code repository includes reproducible pipelines for data generation, model training, inference, and evaluation across standard and downstream benchmarks. Detailed descriptions of the dataset preprocessing, model architecture, hyperparameters, training objectives, and evaluation protocols are included the main paper and appendix, along with extensive ablation studies and full experimental results. All baselines use publicly available implementations following the original authors' specifi- cations, and we additionally release our benchmarking dataset for mixed degradation removal and evaluation protocols for all downstream task analysis.
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15


Reviews:
[{"review_id": "UvJWgjSybg", "reviewer": "Reviewer_atD5", "summary": "The paper presents PRISM, a prompted, controllable diffusion framework for restoring images suffering from compound degradations. The training setup uses mixtures of up to three distortions and introduces a weighted contrastive disentanglement stage to make embeddings separable and compositional. Inference accepts free-form prompts that are mapped to a fixed set of restoration labels; a latent-diffusion backbone performs restoration and a Semantic Content Preservation Module (SCPM) refines fine detail. Experiments cover a new Mixed Degradations Benchmark (MDB), zero-shot evaluation on real datasets (UIEB, under-display camera, and fluid lensing), and downstream tasks across remote sensing, ecology, microscopy, and urban scenes.", "questions": ".", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) Clear objective and method design. The paper argues for simultaneous rather than sequential restoration, emphasizes expert control, and focuses on scientific fidelity rather than aesthetics. The architecture coherently combines contrastive disentanglement, prompt-conditioned latent diffusion, and SCPM for detail recovery.  \n2) Good reported performance and breadth. On MDB, PRISM outperforms representative all-in-one and diffusion/composite baselines (e.g., AirNet, Restormer, NAFNet, PromptIR, OneRestore, DiffPlugin, MPerceiver, AutoDIR) on PSNR/SSIM and perceptual metrics.  \n3) Generalization beyond the synthetic training setup. The paper reports zero-shot gains on real distortions (underwater, under-display camera, and fluid distortions) and shows that performance scales well as the number of simultaneous degradations increases.  \n4) Practical value for scientific analysis. Selective, prompt-guided restoration improves downstream tasks in several domains, supporting the utility of controllability.", "weaknesses": "1) Control granularity and evaluation scope:\n  The evaluation largely uses manual prompting with a pre-defined set of distortion types, not open-ended language or fine-grained controls. The paper itself notes that extending controllability beyond “which distortions to remove” to specifying intensity and spatial extent is left for future work. This leaves unanswered how robust the system is to realistic prompt variations or local/severity-aware edits.\n\n2) Synthetic-to-real gap and capped composition complexity:\n   Training relies on synthetic mixtures and explicitly caps each sample at up to three distortions for efficiency and interpretability. The authors acknowledge that these synthetic augmentations cannot fully capture real distortions. While results on real datasets are encouraging, this cap and reliance on synthetic compositing may underrepresent harder real-world compound effects.\n\n3) Efficiency and deployability are not quantified in the main text:\n   The paper does not provide main-text wall-clock, throughput, or memory comparisons versus strong diffusion baseline. Without standardized timing/FLOP/peak-memory profiles at a fixed resolution, it is difficult to assess practical deployability or the overhead of the added control and SCPM modules.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dWSGaN4RwM", "reviewer": "Reviewer_S4VD", "summary": "The paper proposes a framework for controllable restoration of images that underwent multiple primitive distortions. The restoration of the degradations happens at once, rather than consecutive restorations that may introduce artifacts, yet enables defining which degradation to restore in order to preserve necessary signals.\n\nAs part of the training process, a restoration benchmark with tuples of a clean and a degraded image, along with a natural language prompt that describes the degradation is used, which is made public. Given a prompt, the framework includes using a frozen CLIP text encoder for multi-label classification from a set of primitive degradation, which are then formatted to a predefined form of prompt. This prompt is then used to restore the image by applying a finetuned version of SD 1.5, where the CLIP image encoder was finetuned to cluster embeddings by degradation, followed by an additional model trained to preserve semantic content.\n\nThe overall framework is evaluated on a benchmark with images that underwent distortions as the ones in the training process (up to 3 primitive distortions) and on unseen distortions. Furthermore, its usage for four downstream tasks is evaluated, showing scientific utility.", "questions": "* In addition to PSNR reported in Fig 3, what was the effect on other discussed metrics?\n* What value is used for the number of variants $m$ ? And what is the minibatch size? If these values are not similar there might be added values in weighing the two terms in the denominator of the per-variant contrastive loss to control the effect of repelling from other degradations and that of repelling from other images.\n* Is there a difference between the dataset described in Sec.3.1 and the benchmark described in 3.2 (MDB)? If so, what is included in MDB?\n* In Sec. 4.2, did PRISM identify the same set of primitive distortions for different images from each domain where images probably went through similar degradation?\n* Which images were used to create the visualization of the scatter plot in Fig. 5?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Image restoration is a critical task, particularly for scientific applications. This paper demonstrates the method's effectiveness through general purpose image restoration, evaluated using fidelity and perceptual metrics, and its application for downstream scientific tasks.\n* The motivation is written clearly, and the figures (although 1 and 2 are not referenced) support the understanding of the general approach.\n* Although the number of consecutive distortions in the training set is limited to 3, section 4.2 shows that the method archives good results also for unseen degradations that are not necessarily a combination of the degradation in the train set, or are a combination of more than 3 primitive distortions.", "weaknesses": "* While the fine tuning of CLIP image encoder is explained thoroughly, the following steps of how SD 1.5 is used as the backbone and the suggested SCPM module are explained only briefly. This impairs the understanding of the entire framework, and while the code is submitted, the text itself is not sufficient to reproduce the code.\n* The concept of automatic restoration needs clarification. While the paragraph on prompting (line 207) describes the automatic transformation of natural prompts to fit the required format, the method for generating these automatic prompts remains unclear. Possibly related, it is unclear which prompts were used in the experiments in Sec. 4.1.\n* The motivation of controlled restoration for expert in the loop scenarios could be further evaluated by comparing images that underwent multiple distortions but PRISM is prompted to restore only a partial set (as in Sec. 4.3.1 on synthetically) where the control of the restorations suggests different restorations for specific images / use cases rather than a predefined subset for all images in the same domain.\n\n**Minor:**\n* Indices are not explained and somewhat confusing. It seems $d_{i_j}$ in Eq. 1 denotes a specific distortion and $d^i$ in row 180 denotes a set of distortions, yet the notations are explained only after being used ($d_j$ is explained in line 200).\n* Using the Jacard distance between degradation sets neglects how some distortions are more similar than others.\n* Should mention how the prompts in the dataset are auto generated (line 157).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "x8jTXV3TII", "reviewer": "Reviewer_ztwF", "summary": "This paper introduces PRISM, a controllable diffusion framework for compound image restoration, primarily targeting scientific applications. The method involves a two-stage process: first, fine-tuning a CLIP image encoder using a novel weighted contrastive loss to create a compositional embedding space for degradations , and second, training a latent diffusion model conditioned on these embeddings and user prompts to perform selective restoration.", "questions": "What is the inference time of PRISM compared to the baselines (e.g., MPerceiver, AutoDIR, and the non-diffusion NAFNet), and how do the authors justify this computational cost for the observed performance gain?\n\nGiven the reliance on synthetic data , how can we be sure the model is learning robust compositional reasoning for real-world physics  rather than a complex interpolation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The work compellingly argues for the necessity of controllable, selective restoration over automated 'full' restoration for scientific applications, demonstrating significant gains in downstream task utility.", "weaknesses": "The primary methodological concern is the limited novelty. The core idea of fine-tuning a CLIP encoder to be degradation-aware heavily relies on prior work (e.g., DA-CLIP). The main novelty appears to rest on the Jaccard distance weighting in the contrastive loss, but the paper lacks a direct ablation comparing this to an unweighted compound contrastive loss, making it difficult to isolate its true impact. \n\nSecond, the two-stage training pipeline  is computationally complex, and the choice of a diffusion backbone introduces significant inference latency. This efficiency trade-off is not sufficiently justified, especially as the performance gains over other recent diffusion methods  are notable but not transformative. \n\nFinally, the framework's generalization to real-world, unseen degradations is questionable. The model is trained exclusively on synthetic composites , and it is unclear if the model is truly learning compositional physics or rather a powerful interpolation across its massive synthetic training domain when faced with the complex, non-linear physics of real-world degradations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4VR02LhtyE", "reviewer": "Reviewer_jZpm", "summary": "This paper introduces PRISM (Precision Restoration with Interpretable Separation of Mixtures), a prompted conditional diffusion framework for expert-in-the-loop controllable restoration of images with compound degradations. PRISM integrates two key components: (1) compound-aware supervision trained on mixtures of distortions, and (2) a weighted contrastive disentanglement objective that aligns compound distortions with their constituent primitives. This approach enables high-fidelity joint restoration.", "questions": "1. Collecting a dataset from existing datasets is not very convincing. Have the authors collected any data?\n2. It is a general method that is applied as a unified model for scientific and environmental images. More domain-specific priors, such as physical information, should be considered.\n3. The text prompt is also short for benefiting these domains.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well-structured and the motivation is clear.\n2. The framework leverages compound-aware supervision and a contrastive disentanglement objective across a diverse set of primitive tasks. This produces separable embeddings of distortions, enabling robust restoration, even for unseen real-world mixtures.\n3. This work proposes a novel benchmark for scientific utility, spanning remote sensing, ecology, biomedical, and urban domains.", "weaknesses": "1. The method is devised on the basis of CLIP and Latent Diffusion Model. Moreover, Semantic Content Preservation Module is also relatively simple.\n2. There is no physical information being incorporated into the training process. In other words, it is a general method that is used for scientific domains.\n2. The dataset is constructed by integrating existing datasets, which is not very solid.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TURVHvF6JI", "reviewer": "Reviewer_dhPx", "summary": "The authors argue that the current unified processing pipeline adopted for image restoration in the scientific field may introduce redundant restoration types, which could counter productively lead to the loss of authentic information. Therefore, the authors aim to provide a controllable mechanism, Controllable Diffusion,  to enable experts to select specific restoration types to avoid the aforementioned dilemma.", "questions": "The same as the above analysis in Weaknesses.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "A.The authors present a novel perspective in addressing the issue. \n\nB. The amount of work undertaken is solid.\n>- They constructed a brand-new dataset and employed the latest relevant methods (mostly works from 2024) to demonstrate the superiority of the proposed approach.", "weaknesses": "I think subjective assumptions and the unconvincing problem-solving approach are the most prominent issues.\n\nA.Subjective assumptions\n>-  A.1 Taking underwater image restoration, as mentioned in the article, as an example. The authors identify three types of distortions: low light, scattering, and wavelength dependency. Since these distortions act on the same image, any single restoration method may inadvertently damage useful information. Therefore, the core of fidelity preservation should lie in the precision of each restoration. However, the authors' equivalence of precision with controllability over the quantity and types of restoration is a unconvincing subjective assumption.\n\n>- A.2 It is possible that the most appropriate combination of restoration types has already been implicitly learned by the model through training. However, the authors have neither experimentally nor theoretically refuted the aforementioned viewpoint nor provided substantive support for their own hypothesis.\n\n>- A.3 The authors arbitrarily set the number of distortion types to 3, which is overly simplistic and lacks justification. This approach may fails to flexibly handle complex scenarios.\n\nB.Unconvincing problem-solving approach\n>- B.1 The core idea of artificial intelligence is to employ machine intelligence to replace human thinking. When encountering problems, the focus should be on solving them directly, rather than reversely introducing human labor costs to empower machines.\n\n>- B.2 Although the experimental results show some improvement, these enhancements are not significant. Moreover, there is a lack of relevant analysis regarding the extent to which such improvements stem from the investment in human labor costs and whether they offer cost-effectiveness. Additionally, the ablation experiments fail to fully elucidate the impact of distortion type settings and their dependence on accurate expert guidance.\n\n>- B.3 Assuming your hypothesis is correct, why not just solve the problem in a software engineering way? Let a single model specialize in removing a single type of noise,  we design control codes to allow experts to manually execute several actions to process the images. Therefore, what is the point of incorporating NLP models for processing opinions of experts and coupling the problem together to train a model?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)