Reconstruct Anything Model a lightweight general model for computational imaging
Authors: Matthieu Terris
Samuel Hurault
Maxime Song
Julián Tachella
Venue: ICLR 2026 Poster (2026)
Keywords: computational imaging
deep learning
self-supervised learning
foundation models
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We propose a novel conditioning mechanism enabling to condition a UNet on a measurement equation.

Abstract: Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion methods leveraging pretrained denoisers, and unrolled architectures that are trained end-to-end for specific imaging problems. Iterative methods in the first class are computationally costly and often yield suboptimal reconstruction performance, whereas unrolled architectures are generally problem-specific and require expensive training. In this work, we propose a novel non-iterative, lightweight architecture that incorporates knowledge about the forward operator (acquisition physics and noise parameters) without relying on unrolling. Our model is trained to solve a wide range of inverse problems, such as deblurring, magnetic resonance imaging, computed tomography, inpainting, and super-resolution, and handles arbitrary image sizes and channels, such as grayscale, complex, and color data. The proposed model can be easily adapted to unseen inverse problems or datasets with a few fine-tuning steps (up to a few images) in a self-supervised way, without ground-truth references. Throughout a series of experiments, we demonstrate state-of-the-art performance from medical imaging to low-photon imaging and microscopy.
Our code is available at https://github.com/matthieutrs/ram.

Published as a conference paper at ICLR 2026
RECONSTRUCT ANYTHING MODEL: A LIGHTWEIGHT GENERAL MODEL FOR COMPUTATIONAL IMAGING
Matthieu Terris
Samuel Hurault
Maxime Song
Julián Tachella
Université Paris-Saclay, Inria, CEA Blur Labs
SENS Paris, PSI., CNRS SENSL, CNRS UMR 5672
*CNRS UAR 851, Université Paris-Saclay
-hot parformance
Self-supervised finetuning
Mition blur
Gaursian zomography
Singlecoll
Polsson
Mutiiol MRZ
Setelite Clmosaicing
Blind debtur
SPAD comera
Cryc-EN
metosrer
RAM
In-distributton degradarinn and maagss
OCI degradations
Blnd dearadation 0O0 degradatton and Imasee
Figure I: The proposed Reconstruct Anything Model (RAM) model can solve wide variety of inverse problems, from medical imaging microscopy and low-photon imaging, obtaining state-of- the-art zero-shot performance in imaging problems and datasets in or close to the training distribu- tion. RAM can also be finetuned self-supervised way (without any ground-trun references) for images and/or problems strongly differing from training distribution.
ABSTRACT
Most existing learning-based methods for solving imaging inverse problems can be roughly divided into classes: iterative algorithms, such plug-and-play and diffusion methods leveraging pretrained denoisers, and unrolled architectures that are trained end-to-end for specific imaging problems. Iterative methods in the first class are computationally costly and often yield suboptimal reconstruc- tion performance, whereas unrolled architectures and require expensive training. In this work, we propose a novel non-iterative, lightweight architecture that incorporates knowledge about the forward operator (acquisition physics and noise parameters) without relying unrolling. Our model trained to solve wide range of inverse problems, such as deblur- ring, magnetic resonance imaging, computed tomography, inpainting, and super- resolution, and handles arbitrary image sizes and channels, such as grayscale, complex, and color data. The proposed model can be easily adapted to un- seen inverse problems or datasets with a few fine-tuning steps (up to a few im- ages) in sule-supe way, without ground-truth references. Throughout series of experiments, we demonstrate state-of-the-art performance from med- ical imaging to low-photon imaging and microscopy. Our code is available https://ghhub.commmmtttttuuurrrs
generally problem-specifie
INTRODUCTION
Computational imaging problems ubiquitous in applications ranging from demosaicing in com- putational photography to Magnetic Resonance Imaging (MRT). In this work, we focus on linear inverse problems of the form
y p(y) Az),
(1)
where æ E Rn is the image to recover, E R are measurements, A: R" - R an operator describing the acquisition physics, usually assumed be linear, and p is the noise distribution,

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Published as a conference paper at ICLR 2026
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Figure 2: Proposed architecture for solving non-blind imaging inverse problems. Top row: The architecture builds upon DRUNet backbone, originally designed with convolutional and residual blocks, but is enhanced to integrate knowledge about the measurement operator and measure- ments y. Bottom row: At each scale, feature maps are decoded into the image domain, processed through a Krylov subspace module (KSM), and then re-encoded. The encoding/decoding module consists of simple residual convolutional block. The KSM blocks concatenate power iterations of the scaled measurement operator A As, enabling efficient and adaptable processing for a wide range of inverse problems.
which can typically be Gaussian and/or Poisson noise. We mostly focus on non-blind problems with known A.
The most common approach to solving the inverse problem i is to employ an iterative algorithm minimize an objective function of the form
1
arg min 2 Ar y g(a), x
where g encodes prior knowledge about the data. Classical approaches rely on handcrafted priors such as total variation (Rudin al. 1992) or wavelet sparsity (Mallat 2009). However, recent advances have highlighted the effectiveness of learned priors, particularly deep denoising neural networks, which have been successfully integrated into iterative reconstruction methods. These include Plug-and-Pln (PnP) algorithms (Venkatakrishnan et al. 2013) Rechorst & Schniter 2018
Pesquet et al. 202T, et al. 2021 Hurault et |l.] [2021) and the more recent diffusion models- based methods (Chung et al.] 2022, Karras et al. 2022 Daras et al. 2024 Zhu et al. 2023 The advantage of these approaches that single model trained only for denoising can be used solve large set of image restoration tasks. However, these methods come with several major drawbacks: they are often slow, may introduce blur (Blau & Michaeli, 2018, Milanfar & Delbracio (0024] Terris) et al., 2024), and depend heavily the training data, limiting their in domains with scarce ground truth references (Belthangady & Royer 2019).
Another common strategy for solving I is to train end-to-end deep neural network specifically for the given task. In low-level vision applications such as single-image super-resolution and image denoising, architectures are often derived empirically, with UNet-based models emerging stan- dard choice (Zhang et al. 2021 2023). Notably, the UNet has also become a prevalent backbone for denoising tasks in generative modeling (Song & Ermon 2019: Rombach et al., 2022 Karras al. 2024). However, in computational imaging tasks (e.g.. MRI, computed tomography, astronomical imaging), unrolled architectures, inspired by optimization algorithms, are typically preferred due to their ability to incorporate the measurement operator, A, and observed data (Adler & Öktem 2018 Hammmrnik et al. (2018) Ramzi et al. 2022). Yet, the architecture of unrolled models strongly differs from state-of sfate-of-the-art UNets used for low level tasks. In particular, empirical results (Zhang ctt al. 1 2023 Zbontar et al. 2018 Zamir et al. (2022b) suggest that effective architectures need operations. In both cases, however, even minor archi-
not strictly adhere to optimizatiion-erive tectural modifications such as adjusting the number of input and output channels- -oteen require full model retraining. This reduces adaptability across datasets and inverse problems with simi- lar statistical properties, such as transitioning from grayscale to color images or handling complex multi-channel representations (Liang et al. 2021).
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In this work, we propose novel neural network architecture for solving ① building the DRUNet convolutional neural network (Zhang et al. 2021p. Our key modification introduces a conditioning mechanism that integrates the acquisition physics (A. p) through multigrid Krylov iterations. We train a single backhone model across multiple imaging tasks simultaneously ranging from image deblurring to MRI, handling datasets with grayscale, color, and complex images, corrupted by both Gaussian and Poisson noise. As result, our architecture supports grayscale (I channel), complex- valued (2 channels), and color images (3 channels), with only the output heads differing to accom- modate specific channel numbers. Beyond its strong zero-shot performance, we demonstrate that the model can be efficiently fine-tuned in a self- supervisedi way on real measurement data.
2 RELATED WORKS
End-to-end architectures Several architectures have imposed themselves as cornerstone in the low-level vision community. Early breakthroughs include the UNet (Ronneberger al., 2015 Jin et al. 2017) and deep residual convolutional networks (Zhang et al. 2017a| 201 8b] 2017b) More recently, architectures incorporating attention mechanisms have surpassed previous state-of-the-an models (Zhang et al. 2023; Liang et al., 2021 Zamir et al. 2022b). Akin to our work, (Ren et al.) 2023 propose to condition denoising diffusion UNet a multiscale version of the measurements. Notable recent developments include transformer- based models, particularly in single-iman imale super- resolution (Liang et al. 202T; Zamir et al.] (022b), and, more recently, state-space models (Guo) al. 2024). Despite these advances, the UNet remains key backbone for many state-of-the-art methods, see e.g. (Zhang et al. 2023 Zamir et al. 2022b), incorporating transformer blocks on UNet-like architectures.
Unrolled architectures To better condition on the acquisition physics, unrolled networks intro- duce learnable neural modules (e.g., convolutional layers) within the iterations of optimization algorithm solving 2 Several architectures have become standard in this framework, notably in
natural image processing (Zhang et al., 2017b: 2020; Bertocchi et al., 2020 Sanghvi et al. 2022), medical imaging (Adler & Oktemi 200 8. Ramzi et al. 2022] Gossard & Weiss, 2024). and astro- nomical imaging (Aghabiglou et al., 2023). However, these models have large memory footprints and are typically trained on single or limited set of tasks with limited variations. Moreover, there a significant discrepancy between the overall architecture of these unrolled models and the highly effective architectures employed by the previously discussed state-of-the-ee UNet-like architectures.
Self-supervieed learning In many scientific and medical imaging applications, obtaining ground- truth data for supervised training is often expensive even impossible (Belthangady & Royer 2019). Self-supervised methods enable training directly from measurements alone (Batson & Royer 2019 Chen et al. 1 Tachella et al. 2023b). In this work, we show that self superviierv is highly effective for finetuning foundation model without ground-truth references.
General image restoration models Recently, various papers have proposed image restoration models that can handle multiple tasks using the same underlying weights (Potlapalli et al. 2023 Hu
Cui et al. 2025). While these works share similar goal of building foundation models, they differ from this paper in that they focus on (semi) blind image-to-iman
al. 2025
tasks, such as denoising, deraining, and dehazing. In contrast, the proposed model designed handle scientific and medical imaging tasks where the measurements are not necessarily in the image space and the observation model (1) typically known, such as in MRT (k-space data) or CT (sinograms). Another recent line of work pushes beyond standard denoising priors by integrating general restoration models into iterative schemes (Hu et al.) 2023 2024 Terris et al.) 2025). While these approaches use training tasks similar to ours, they rely model trained for single restoration task. In contrast, method learns a single architecture capable of handling multiple tasks in a non-iterative framework.
PROPOSED APPROACH
3.1 UNROLLED ARCHITECTURES
Unrolled architectures propose to "unroll" an optimization algorithm for solving equation 2 mixing interpretable blocks and learnable blocks accounting for g. This yields an architecture of the form
(yy = Lox o D(..)) .... o Lo, o D(A y. y).
(3)
where (Lo, Jocksk K are learnable layers, and D(. y) is a data- fidelfidelity enforcing term, ensuring that the iterates satisfy equation [
Two main choices for D arise in this context: the first one is
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Id V=VV-V((.))) the second one is D y. where prox denotes the proximal operator
Combettes Pesquet 2011 Parikh al. 2014). f(x, y) A 211 and À > 0 is constant. Using the proximal operator allows to accommodate choose non non-differentiable data terms for f, which can be useful practice. For instance, in the noiseless case, one can to choose as the indicator function of the set C {æl Ar y). In turn, D acts as a projection on C, ensuring that noisy case, one can instead choose {ar|l Ax yll S
satisfies the measurement equation. e). where > 0 is user-defined constant.
From a wider perspective, these architectures benefit from an interpretable conditioning on the mea- surements y and the operator A, without any restrictive assumption on the operator A. For instance, these do not impose matrix form for A, nor an SVD decomposition. However, they strongly differ from now established architectures that have demonstrated tfte-of-le of-e--of-the-ar the--rt performance low-level vision tasks (Zhang et al.) 2021] Zamir et al.] 2022b] Potlapalli et al.) (023). In this work, we aim reconciling these two views, and propose leverage tools from unrolled architectures design to condition a standard backbone model on y. A and statistics of the acquisition noise.
3.2 PROPOSED ARCHITECTURE
The proposed architecture, illustrated Figure is derived from the DRUNet Zhang al. 2021 In this section, we detail the proposed architectural modifications to handle general inverse prob- lems. First, we add proximal estimation module casting the imput into a form. In subsequent layers, the conditioning is performed via Krylov subspace iteration on the feature maps. Finally, we adapt the architecture order handle a variety of noise models.
corsueed
Initialization with a proximal estimation module In order to solve ①I for wide variety of in- verse problems, a first step is to map y onto the appropriate image domain. Several approaches are used in the literature, using either A y the input of the network or A y. The first approach shows the advantage of inverting the ill-conditioned A, turning the effective problem for subsequent back- bone layers Gaussian denoising problem. However, this pseudo-inverse operation is extremely sensitive to noise. In noisy settings, the latter approach is often chosen, at the cost of blurring of the input. Instead, letting fxx y) = Al yl12. our first estimation step consists proximal step
prox (A A yy) arg arg min Al| Au yyl² + || A Tyl:
where À 0 is regularization parameter. Equation (4) be seen middle-groum between A (when À is low) and A' (when A is large). We set À proportional to the imput signal-tonooin ratio (SNR) as A on/ ///l where is the Gaussian noise level and r) is learnable parameter.
Krylov subspace module Given an intermediate estimate of the image x', unrolled network ar- chitectures condition on the acquisition physics A via a gradient step on an L distance data-fidelit term, i.e.
x 7A (Ax. y),
a
or via proximal step, i.e.
arg min 7 Au y/1² + ||ue 22112.
for some stepsize 7. The proximal step commonly solved using K iterations of conjugate gradient,
yielding solution in the Krylov subspace spanned by [(T + () y + x: x steps can written as
k== and both
K
On (AT AATA + A)*AT ATy,
(5)
k=0
for some scalar coefficients Bk) Thus, we propose to condition on A using Krylov Subspace Module (KSM), which learns the linear combination coefficients ((xx } Given an intermediate latent representation, decoding module first maps the features onto the image domain
using a convolution layer. Then, it stacks { ((AT a (A (ATA)* A y H along channels and combines them through a convolution. The output is finally added to the latent space via a convolutional encoding module, Figures and for more details.
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Multiscale operator conditioning For fixed number of measurements m, the finer the grid (i.e., larger rt,, the more ill- ill-posed the inverse problem becomes, as the dimension of the nullspace of A is lower bounded by to condition our architecture on forward operators defined on coarse grids as:
Thus, inspired by multigrid methods (Hackbusch 2013). propose
A. = AU,
(6)
where U, : R
R is a 8x upsampling operator with a Kaiser-windowed sine antialias filter (Kaiser & Schafer 1980). Figure illus-
Aly
A,y
trates that the linear pscudoinverse is unstable on fine grids but remains stable coarse grids. We normalize all operators to have unit norm at each scale, i.e., |A, ||2 for all Moreover, for many inverse problems, we can develop ef- ficient implementations of A A, R # + and A, y E * which can be computed fully on the coarse grid, avoiding any expensive fine scale computations. For example, if A repre- sents blur or an inpainting operation, we can simply downscale the kernel or the inpainting mask, respectively. We stress that, in the case where K = O, the proposed method amounts to condition inner feature maps on y only. In this case, recover the setup of (Ren et al. 2023).
COSTTeT
Figure 3: Multiscale conditioning. Estimating the underlying image (here motion blur) is easier on coarser grid than a fine grid, as the forward operator is more ill-posed the latter case.
Noise conditioning & biases Same as the original DRUNet (Zhang et al. 2018a; 2021). condi- tioning the noise level is implemented by concatenating constant feature maps, filled with the noise level, along the channel dimension. While a single noise map was added for Gaussian denois- ing in (Zhang et al. 2021), we extend this strategy to two maps, corresponding the parameters and 7 of the Caassonn noise model
y = y= + on,
where 7 2 is the gain facto associated to the Poisson noise Z P(x/7), and o 2 0 is the standard deviation of the Gaussian component of the noise, with (O f). All biases are removed from our architecture to ensure scale equivariance with respect to both o and components, enabling better generalization to unseen noise levels (Zhang al. 2021 Mohan al. 2020
Shared layers between imaging modalities The modifications needed to handle inputs with dif- ferent channel numbers (color, grayscale, complex images) involve only small subset of the model parameters. Specifically, only the first (input) and last (output) convolutional layers of the DRUNet, as well the encoding and decoding blocks within the KSM modules, are adjusted to account for varying channel numbers. All other weights of the model are shared across modalities.
4 TRAINING
4.1
SUPERVISED TRAINING
We train our network simultaneously on G computational imaging tasks, spanning image restoration (deblurring, inpainting. CT. and others (see Table|5]oor a complete list), leveraging the DeepInverse library (Tachella al. 2023a). Each task g associated with a dataset Dg = { LSDIR (Li et al. 2023) for tasks involving natural images, LIDC-IDRI for CT images (Armato III et al. 2011), and the fastMRI brain-multicoil dataset for MRI (Zbontar et al., 2018).
denoising in both color and grayscale), single-coil MRI,
Furthermore, each task g can be framed as an inverse problem for some A, and noise following the Poisson Gaussian distribution (7) with varying gain 7 2 and standard deviation 2 0. Our model is trained in a supervised fashion to minimize the following task-wise loss:
(0 xigg) = Baaa (.) By æ..a Wyll Ro Ag, gg: 7/) ziglli
We use the convention that for 0 the noise model becomes purely Gaussian.
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CBSD68
Urban1000
Div2K
CBSD68
Urban 100
Div2K
Method
Params
Med.
Hard
Easy
Med.
Hard
Easy
Med.
Hard
Easy
Med.
Hard
Easy
Med.
Hard
Easy
Med.
Hard
End-to-end lterative
DPIR
32M
333.45 26.53 24.42
24339 28.12 24.38 [36.32
34.39
29.97 27.16
32.10 25.70 22.73
32.53
23.78
20.38
35.23
28.14 24.71
DitpIR
552M
31.83 25.98 23.80
32,93
27.03 23.69
30.93 24.96 22.51
23.63
20.32
DDRM
552M
33.10
25.41
34.19
29.65
26.05
35.19
30.57 27.70
31.85 25.89 23.
32.73
24.40
20.73
34.46
28.25 24.89
456K
26.84 24.62
28.36
12.66
29.88
22.81 19.90
24.03
Restoemer
26M
50.96
26.99 25
28.21
24.75 23.13
28.56
25.75 25.00
31.68 25.93 23.19
29.07
24.00 20.99
30.01
27.20 25.00
32M
35.79
31.90 26.10
24.31
.5
28.45
uDPIRAU
256M
33.64
27.90
25.5 54
52.72
28.14
25.28
35.38
30.17 27.77
31.86 26.03 23.38
30.26
24.32 21.04
34.47
28.37 25.23
RAM
36M
34.04
28.22
64
33.61
28.63
25.30
36.18
30.72 27.89
32.59 26.19
31.78
35.23
28.56 25.16
Table 1: Results motion (left) and Gaussian (right) blur. Best result in red second best in blue. Results in bold indicate iterative methods that outperform the proposed method. "DDRM assumes access to SVD decomposition of the operator; for faimess, adapt the blur problem to allow FFT-based convolution.
where Ro is the model be trained, Ag is the measurement operator of task g. and (a:7) the noise parameters sampled from a distribution p(o, We choose the & over the standard &g loss it was empirically observed to obtain better test performance (Zhao al. 2017). We consider weighting parameter wig = || y 22/0g a to ensure the training loss is balanced across different noise levels and tasks. The final training loss obtained by summing over all tasks:
G N₂
( - Σ1 60 Z.
(8)
This formulation allows the network to generalize across multiple imaging modalities by learning from diverse measurement operators and noise distributions.
For each of the training inverse problems, we extract a random image patch Figg of size (C, 128, 128) with E 2,) to which we apply the measurement Ag. We use the pretrained weights of the DRUNet denoiser to initialize our model. The model is trained with a batch size of 16 per inverse problem considered and for a total of 200k steps. We use the Adam optimizer with learning rate 10-1, which is divided by 10 after 180k steps.
4.2 SELF-SUPERVISED FINETUNING
Many real computational imaging applications have limited or no ground-truth reference data (Belthangady & Royer 2019). In such cases, the RAM model can be finetuned using mea- surement data alone, leveraging some of the recent advances in self-supervised learning for inverse problems (Hendriksen et al. [220 Huang al.]|202. [202]]Chen Chen al. [021] Tachella al., 2022 2024). In particular, we can finetune our pretrained RAM model with loss that only requires dataset of noisy and/or incomplete observations f y:
N Σ
((
L0 y.) + w (O,
where is trade-off parameter. The first term, LMC, enforces measurement consistency At x Ax while taking care of the noise, and is chosen according to the knowledge about the noise distribution of the finetuning dataset, i.e., using SURE (Stein) 198T) for fully-specified noise models, and UNSURE (Tachella et al.) (224)) or splitting losses (Batson & Royer 2019) in the case of unknown or mispecified noise models. The second term, ENULL is required A is non- invertible and handles the lack of information in the nullspace of A. by leveraging information from multiple forward operators (Tachella et al. (023b) and/or enforcing equivariance to a group of transformations CChn et al. 2021). More details provided in Appendix E.1
RESULTS
5.1 BASELINES
We evaluate our model's performance alongside both iterative reconstruction methods, unrollled models and end-to-end architectures. First, consider the Plug-and-Play model DPIR (Zhang

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Published as a conference paper at ICLR 2026
A A y
DPIR
uDPIR tied
DDRM
RAM
Ground-trute
Motion Blur
25.1
25.7
25.4
25.7
PSNR (dB)
Gaussian Blur
30.8
31.2
30.7
31.5
PSNR (dB)
Figure 4: Deblurring results. Top row: motion blur hard, on a CBSD68 sample. Bottom row: Gaussian blur medium, DIV2K sample.
MRI x4
MRI x8
SR4
mcMRI
Poisson
Method
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Clean
Noisy
Method
PSNR
SSIM
PSNR
SSIM
PDNet
DPIR
28.25
0.7119
24.54
0.641
23.09
0,713
PDNet
13.20
0.357
aDPIR-:
34.14
33.73
0.848
0.851
30.86
30.20
0.805
0.792
28.35
27.00
0.779
0,772
uDPIR-antied
UNet
uDPIR-die
28.50
33.71
0.850
0.662
14.62
0.462
0.446
SWINIR
26.16
25.47
RAM
34.39
0.853
31,50
0.813
28.83
0.798
26.04
25.47
RAM
35.62
0.889
28.83
0.798
Table 2: Left: In-distribution tasks. MRT tasks are evaluated on the fastMRI validation set, CT tasks the LIDC-IDRI test set, and SR4 on CBSD68. Right: Out-of-distribution tasks, namely multi-coil MRI with acceleration factor 8 on fastMRI validation set, and computed tomography with Poisson noise on LIDC-IDRI test set.
et al. 2020), which plugs DRUNet, trained only for denoising, within 8-step Half Quadratic Splitting (HQS) algorithm (Aggarwal et al. 2019), thereby requiring approximately 8x as many FLOPs and being 3.7x slower inference in comparison to model (see Tables 8 and for details). We also compare with unrolled versions of DPIR (referred to as uDPIR (Zhang et al. 2.200). also comprising eight HQS iterations, that we train the same tasks, with the same loss function and dataset as our model. Specifically, we investigate both the weight-tied variant, where all eight iterations share the same DRUNet parameters, and the weight-untied variant, where each iteration has its own set of parameters-resateeeee resulting in a model with eight times more parameters and FLOPs than RAM. Additionally, we compare our approach with the PDNet unrolled network (Adler & Öktem 2018). trained on the same tasks. Finally, we evaluate Restormer (Zamir et al. 2022a), a mansformer-basee model with similar computational cost (in terms of FLOPs) to DRUNet, but trained separately on each restoration tasks. We also compare the proposed model with the diffusion- based DDRM algorithm (Kawar et al. 2022) with the diffusion model backbone from (Dhariwal & Nichol 2021); see Appendix[Dfor for technical details adaptation of DDRM to the proposed setting.
5.2 IN-DISTRIBUTION RESULTS
We first evaluate the proposed method on tasks that are consistent with the training tasks.
Deblurring We evaluate two types of deblurring: Gaussian deblurring, using fixed Gaussian blur kernels, and motion deblurring, based on random motion blur kernels. For each, we define three difficulty levels (easy. medium, hard), determined by kernel characteristics and the Gaussian noise standard deviation. Details for each setup are provided Appendix B.2 Figure and Table report visual and quantitative results. Among non-iterative methods, RAM and uDPIR-tied achieve the best performance, while uDPIR-untied underperforms despite its larger parameter count. Our method generally surpasses other end-to tnd approaches, except for motion deblurring Urban100. Compared to iterative methods, our approach is superior except at low noise levels (a = 0.01) on large images (Urban 100, DIV2K).

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MRI We evaluate the fastMRI brain validation set using the single-coil accelerated acquisition procedure from Zbontar et al. (2018) with acceleration factors 4 and 8. The problem is formulated y diag F an, where is binary mask and F the discrete Fourier transform, and
a = 5 10 Quantitative results are reported in Table 2 and visuals in Figure 9 The proposed
RAM model outperforms all baselines.
CT We evaluate on computed tomography with Gaussian noise, modeled as y = Ax + an, where A is the Radon transform. The acquisition uses 51 angles, i.e., about 10% of the LIDC-IDRI image width, with o = 10-4 as in training. Results are reported in the rightmost column of Table 2 and visuals Figure Io DPIR results are omitted due to instability on this task. The proposed RAM model produces reconstructions with finer details than uDPIR-tied.
Additional results for in-distribution tasks on Doisson-Gaussian denoising, motion blur, super- resolution, and inpainting are provided in Appendix G
5.3 ZERO-SHOT PERFORMANCE ON OUT-OF-DISTRIBUTHIN TASKS
Multi-coil MRI We consider the Cartesian multi-coil MRI problem. In this setup, and assuming L coils, the signal measured by each coil E {1, } writes y; diag (m) F Fdiag((s)) + ong where the (st)igeEL are the sensitivity maps (or S-maps). We provide reconstruction metrics for the multi-coil setting in Table and associated visuals in Figure TO We provide comparisons with the baseline UNet from Zbontar et al. (2018). Both methods perform similarly up to the addition of mild residual noise with the UNet, yielding lower PSNR despite similar visual results. We stress that since PDNet contains learnable layers acting in the measurement domain, one would need to retrain the architecture for this new setting specifically, hence we do not present the results.
Computed Tomography with Poisson noise While model trained the CT problem with Gaussian noise, we propose to apply it to a CT problem degraded with Poisson noise only. As previously, consider a problem with 51 angles. Numerical results are provided in Table 2 and visual results in Figure I0 The proposed method performs better at estimating the textures.
Additional results for out-of-distribution tasks are provided in Appendix G
5.4 ZERO-SHOT PERFORMANCE ON BLIND AND NON-LINEAR INVERSE PROBLEMS
Despite being trained to solve non-blind linear inverse problems, the proposed model can be easily extended beyond this setting.
Blurry
RAM output
Blind deblurring The proposed model can be used for solving blind deblurring prob- lems, where the forward operator A is unknown space-varying kernel, by leverag- ing a blur prediction network (Carbajal et al. 2023) that provides estimate A. Figure shows blind deblurring results on a real mo- tion blur dataset (Köhler al.) 2012) us- ing the spatially-varying blur estimator pro- posed by Carbajal al. (022)). Despite being trained on exact forward operators, RAM ob- tains good results using inexact estimates of the operator. Additional results are presented in Appendix G
Figure 5: Blind deblurring. Results on a real motion
blur (Kohler dataset).
Phase retrieval Although the model is only trained to solve linear inverse problems, can still be applied to non-linear problems that can be decomposed into a sequence of linear problems. We illustrate this idea with the phase retrieval problem y = A(x) = Bx with random matrix E Cmxn and complex E C Inspired by the Gerchberg Saxttnn algorithm (Gerchberg) 1972). we
can iterate between updating the estimate of x given an estimate of the phase of y, and updating the estimation of the phase given æ (see Algorithm 2). Appendix] Dariixxppeeess presents results varying the number of measurements, showing that RAM can obtain stable estimates for low m /n ratios, where standard methods such as gradient descent fail.
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DRUNet
RAM
A
Baseline
zero-shot
Finetuned
zero-shot
Finetuned
Reference
SPAD
Cryo-EM
Comp. Sens.
Figure 6: Self-supervised finetuning. RAM can be finetuned few real LinoSPAD Cryo- EM images without ground-truth nor full knowledge of the noise distribution. Adaptation to new problems (compressed sensing and demosaicing) and new distributions (Sentinel 2 images), by fine- tuning on measurements of a single image.
Compressed Sensing
Demosnicing
Model
DPIR+
DRUNet rand
DRUNet
RAM
DPIR+
DDRM
DRUNer rand
DRUNet
RAM
Sup.
Self.
Sup.
Sup.
Self.
Sup.
Sup.
Self.
Sup.
Self.
zero-shot
31.29
8.07
10.23
22.50
32.84
30.64
8.10
10.73
29.49
N 1
20.92
21.20
19.49
19.44
30.77
30.40
23.14
21.28
25.82
25.57
34.46
33.89
32.52
32.29
24.85
100
22.73
22.24
28.38
30.58
33.40
33.57
25.84
23.22
34.06
34.56
35.26
35.10
Table 3: Effect of finetuning dataset size and self-supervision. RAM requires a few observations obtain good results, whereas finetuning a DRUNet denoiser requires significantly larger datasets. We report average test PSNR in dB.
5.5 SELF-SUPERVISED FINETUNING ON OUT-OF-DISTRRBUTIONN TASKS
Real imaging problems may involve inverse problems or images that significantly differ from those at train time. In this case, finetuning with only handful of measurements (i.c., without ground- truth data), sometimes a single one, substantially improves performance. We present results for three different tasks in Figure 0 and Table 3 In all cases, finetuning with fewer than N 10 measurements results in performances that significantly outperform PnP and diffusion baselines. Moreover, finetuning takes few minutes on single consumer-grade GPU (see Table
Compressed sensing on Sentinel-2 data: on 100 Sentinel-2 RGB images (128 x 128, 10m res- olution), we set A Sdiag(m) with m random #1 mask and S subsampled sine transform (factor 4), With Gaussian noise a = 0.05, RAM finetuning achieves strong results from a single image, clearly surpassing DRUNet. Cryo electron microscopy: we use 5 noisy Cryo-EM micro- graphs (7676 x 7420) from Topaz-EM (Bepler et al.) 2020). where the SNR is very low. Since the noise distribution is unknown, we finetune with splitting loss. Low-photon imaging: we use LinoSPAD images (Lindell et al. 2018) (256 x 256). While noise is generally assumed to be Pois- son in the low-flux case, the images were acquired on high-flux conditions and follow an unknown discrete noise distribution. Due to missing pixel lines, recovery amounts to inpainting and denois- ing. We finetune with a splitting and multi-operator losses (removing lines at random). Further implementation details provided in Appendix E.2
ABLATIONS, UNCERTAINTY QUANTIFICATION, AND LIMITATIONS
Architectural ablations Table shows training PSNRs obtained on all training tasks after a fixed budget of 1 iterations for various architectural variations. We first note that introducing a proxi- mal input block leads to a substantial improvement (+0.8dB). Conditioning the inner feature maps on the observation y provides smaller but consistent improvement (+0.ldB), an observation in line with (Ren et al., 2023). The largest performance boost (+0.9dB) comes from incorporating our Krylov blocks, which apply A A operations to the feature maps. This highlights that mimick-

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Published as a conference paper at ICLR 2026
Configuration
PSNR (dBB
@Params
Training tasks
Inpainting
Deblurring
SR (x2)
(DRUNet backbone)
25.83
32.6M
lopainting only
30.84
23.03
26.17
base + prox base prox embeed
26.64
32.6M
Debluminn only
14.06
25.62
28.02
26.71
33.4M
SRx2, 0 only
n/a
21.25
28,
base + prox + embed y Krylov (RAM)
27.61
35.6M
All three tacks
30.73
25.58
29.79
Table 4: Ablations. Left: Architectural ablations. Average training PSNR on the training set for different architecture variants. Right: Training task ablation. Performance of models trained on different task subsets on the validation set.
ing the core operations of iterative optimization methods within the architecture yields significant benefits. Interestingly, our model surpasses unrolled architectures that are explicitly derived from optimization algorithms. Comparisons with a PromptIR backbone are provided in Appendix C
Single vs multitask training shows that given ap- propriate conditioning the measurement operator, bias towards specific training tasks is negligible, as the model trained on all tasks shows performance comparable (yet slightly inferior to) models trained on specific tasks only. The proposed reweighting using the SNR of the consid- ered problem limits bias towards harder tasks.
measuremeetts
predicted
image
errors
OITOrS
Uncertainty quantification We can use existing uncer- tainty quantification (UQ) algorithms that only require general reconstruction function. Figure 7 shows esti- mated errors using the recent equivariant bootstrap tech- Pereyra 2024) (see Algorithm [ on noisy image inpainting task (DIV2K validation images with o = 0.02 and 50% observed pixels). More details about the bootstrapping algorithm and additional results are included in Appendix H As shown in Figure 19 our algorithm obtains celterecali uncertainty estimates than DDRM, while requiring x00 fewer network evaluations.
nique (Tachella
Figure Uncertainty quantification. Equivariant bootstrapping with RAM pro- vides estimates of pixelwise errors that are close to the true errors.
L Limitations Training the RAM model requires fair amount of GPU resources, which might not be available to all practitioners. Nevertheless, our model can be finetuned on single mid-sized GPU, obtaining competitive results with a few optimization steps on small finetuning datasets. We focus on low-distortion reconstructions, in contrast to the higher perceptual quality and higher distortion of diffusion methods (Blau & Michaeli 2018). However, reconstructors such RAM can be used within sampling algorithms, e.g. (Delbracio & Milanfar 2023] Ohayon et al. (025).
7 CONCLUSION
We present a new lightweight foundational model for computational imaging that achieves state- of-the-art performance on a wide range of problems, outperforming PnP methods while providing similar results 8x more compute and parameter-intensin strate competitive performances without following the common practice of unrolling an optimization algorithm, resulting in a faster and lighter reconstruction network.
unrolled networks. Our results demon-
Our model displays transfer capabilities, as it can be finetuned on new datasets or imaging problems with small datasets (up to single image) in a fully self-supervised way, i.e., without any ground- truth references. These results challenge the idea that a reconstruction network has to be specialized to a specific imaging task, showing good reconstructions across a wide variety of imaging modalities with relatively lightweight network (36M parameters). Our results suggest that imaging tasks that might seem very different (e.g. cryo-EM denoising and demosaicing of satellite images) share significant common structure and can be tackled with the same model.
We believe this work paves the way for new approach to imaging, where most of the effort can be put into developing strong base models and robust self-supervised finetuning techniques.
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ACKNOWLEDGEMENTS
M. Terris acknowledges support from the BrAIN grant (ANR-20-CHIA-0010)) M. Song acknowl- edges support from the PNRIA CNRS project. J. Tachella acknowledges support from the ANR grant UNLIP 23-CE23-0013, This work used HPC resources from GENCI-IDRIS (Grants 2024- AD011014344RI, 2025-AD01101119RR 2025-AD01101682
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Noise Distribution
Task
Dataset
Configuration
Gaussian
Poisson
0
Yiin
Y/max
Denoising
LSDIR
0.001
0.2
0.01
1.0
Grayscale
Deblurring
LSDIR
Random motion & Gaussian kernels (31x31)
0.001
0.2
Inpainting
LSDIR
Bernoulli masks: p (00.3, 0.9)
0.01
0.2
0.01
1.0
SR
LSDIR
Bicubic/billinaa downsampling (x2, x4)
0.001
0.01
Tomography
LIDC-IDRI
Radon transform, 10 projection angles
0.01
Denoising
LSDIR
0.001
1.0
Complex Color
Deblurring
LSDIR
Random motion & Gaussian kernels (31x31)
0.01
0.2
Inpainting
LSDIR
Bernoulli masks: (0.33, 0.9)
0.01
0.2
0.01
1.0
LSDIR
Bicubic/bilincar downsampling (x2, x4)
0.001
0.01
Pan-sharpening
LSDIR
Flat spectral response
0.01
0.1
MRI
FastMRI
Acceleration masks (x4, x undersampling)
0.001
0.1"
Denoising
FastMRI
0.001
0.1"
Inpainting
FastMRI
Bernoulli masks: p (.0.1,
0.01
0.1"
Table 5: Summary of training datasets and inverse problems. Noise parameters: when both mmin and max are specified, dg (mii maax]; when only max is given, dg = o max- Same notation applies to Poisson noise parameters 7g. "for fastMRI, the effective noise levels need to be rescaled by factor 5 10-3 due to rescaling of the full dataset. "_" indicates no noise of that type.
A PRE-PROCESSING OF TRAINING DATASETS
LSDIR We use the LSDIR dataset (Li et al. 2023 for training on natural imaging tasks, consisting of 84,991 high quality images. We split the images in 512x512 non-overlapping patches for faster data loading. Beyond this, no additional pre-processing is performed on the dataset.
We perform virtual coil-combination on the raw kspace data from the brain-multicoil fastMRI dataset (Zbontar al., 2018). We the resulting 70,748 complex images from the training set for training our model and the 21 21,842 slices from the validation set are reserved for validation.
MRI
LIDC-IDRI We the LIDC-IDRI dataset (Armato III et al. tomography experiments containing 244,526 chest slices. We normalize the seans in Houndsfield Units (HU) using the rescale slope and intereept provided in the Dicom files. Data then clipped in the lung window values [-1200, 800] and the rescaled in [0, IJ. We perform random extraction of slices from the dataset to provide (95%, 4%. 1%) split of (training, validation, test).
2011) a basis for our computed
B CONSIDERED INVERSE PROBLEMS
B. TRAINING INVERSE PROBLEMS
We provide Table 5 detailed list of all datatets and inverse problems considered for the training
of RAM.
B.2 BLUR TASKS DEFINITION
The deblurring inverse problem is y k on, where k is some blur kernel and NO0, I)- We use a "valid" padding strategy, i.e., the image not padded before convolving with the blur kernel. In each case, establish types of problems (easy, medium and hard) corresponding to different kernel lengths and noise standard deviations.
Motion deblurring
We generate random motion blur kernels as random Gaussian processes fol- lowing (Tachella et al. 2023a Schuler al. 2015) of 31 pirels.. In this case, the difficulty of
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the deblurring problem is defined by the length scale of blur trajectories E, the standard deviation of the Gaussian processes $ generating the psf and the standard deviation a of the additive Gaussian 0.1, 0.01), {0.6, 0.5, 0.05). {1.2, 1.0, 0.1} for the easy, medium and hard settings respectively.
noise. The tuple {f,s, o are set to 0.1,,
Gaussian deblurring We generate fixed blur kernels with psf of size 31; the difficulty of the problem is defined by the standard deviation of the (Gaussian) blur kernel Oblur and the standard deviation a of the additive Gaussian noise. The tuple {antur o is set to {1.0, 0.01). {2.0, 0.05), (4.0, 0.1) for the easy, medium and hard settings respectively.
Blind deblurring In the blind deblurring experiments, we consider the spatially-varying blurs of Carbajal et al. (2023), defined as
25 Ax =
k: (wi x))
1=1
of blur kernels of 33 33 pixels, and {wi) is a of spatial masks with values between O and matching the size of the input image.
where (1: 25
C COMPARISON WITH PROMPTIR BACKBONE
In the setting of Section with fixed training budget, provide in Table metric for Motion deblurring on CBSD68. The DRUNet backbone performs on par with PromptIR. A significant improvement is brought by the proposed conditioning. Associated visual results are provided in Figure in the a = 0.05 case. We notice that beyond border effects, no significant visual difference noticed between the PromptIR and DRUNet backbones: both reconstructions show hallucinated archs that are present in the measurement but not in the groundtruth. The proposed conditioning resolves this issue.
PromptIR
DRUNet
RAM
Motion blur a 0.01
22.84
22.99
30.78
Motion blur a = 0.05
22.42
22.46
26.47
Motion blur a 0.1
21.98
22.05
24.73
Table 6: PSNR on the BSD68 dataset for different architectures. The three architectures are trained in identical setups.
D ADAPTATION OF DDRM
The DDRM algorithm builds denoising diffusion UNet backbone, following the architecture from (Dhariwal Nichol 202T), as (Kawar et al. 2022). We use the pretrained weights provided by the authors of (Kawar al. 2022). Due to its large receptive field, this convolutional UNet requires sufficiently large input sizes, but memory constraints make resolutions beyond 512x512 impractical. To address this, we use circular padding align inputs with the 2 minimum size constraint, and process larger images by dividing them into non-overlapping 512x512 patches.
E SELF-SUPERVISED FINETUNING
E. SELF-SUPERVISED LOSSES
In this section, we review the possible choices for Lmc and LNULL that can be used in 9
Measurement consistency LMC can be selected based on prior knowledge of the noise distribu- tion Tachella al. 2024). If the noise distribution is known exactly, Stein's Unbiased Risk Estimate
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PromptIR
DRUNet (no conditioning)
(20.56, 0.63) DRUNet + proposed cond.
(21.72, 0.68)
(PSNR, SSIM)
(26.46, 0.74)
Figure 8: Backbone & conditioning influence. Reconstruction results on a motion blur measure- ment y (top left) from the BSD68 dataset. Bottom center image shows the proposed RAM architec- ture (DRUNet backbone with proposed conditioning).
(SURE) Steii 1981 is used. For the case of Gaussian noise of level a, SURE is defined
SSURE (0, y.) A (y y. A.) 1 2o A, o RB) A,),
where the divergence is approximated using Monte Carlo method (Ramani al. 2008 Extensions of SURE to unknown (Tachella et al., (224)) and other noise distributions also exist (Monroy et al., 2025]. If the noise distribution is only assumed to separable across measurements, we can use splitting loss
LSPLIT (0 y:) = Em [I diag(m)) (A, Re Re(diag(m))) diag(m)...) - yt) 112
where m is splitting mask sampled from a Bernoulli random variable or using
strategies (e.g., Neighbor2Neighhoor (Huang et al. 202T. SSDU (Yaman et al. 2020), etc.).
Learning in the nullspace If the finetuning dataset is observed via a single operator A, = A for all 1....,,, .N. we choose LNULL as the Equivariant Imaging (EI) loss (Chen al. 202 which enforces equivariance to group of transformations {Trirew hrew such as rotations or shifts with
La (0) E,-R TT,ē, Ro Ro A)]
where è = (D A.) is the reconstructed image. To learn in the nullspace of A, the group of transformations should be chosen such that not equivariant. If the finetuning dataset asso- ciated with multiple operators {A, (Ar)ER, we choose LNULL. the Multi Operator Imaging (MOI) loss (Tachella et al. 2022), i.c.,
( FerwRlaas Ro A,)
also where 0..0 Ro(y, A,), which enforces consistency across operators R Ro A,) Ro (A,E, A,) for all pairs (i, r).
E.2 EXPERIMENTAL DETAILS
In all finetuning experiments, we use the Adam optimizer with the same parameters used at train time (see Section 4.1). In all experiments and basclines, choose the network checkpoint that
We do explicit the dependency of Ro (a,7)-
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obtains the best performance, using ground-truth for the Sentinel experiments and visual inspection for the SPAD and Cryo-EM data. The trade-off parameter in (9) is set as w = 0.1 in all experiments where a LNULL. loss is used. Shifts up to 10% of the image width and height are considered in the EI loss. Figure shows reconstructions with finetuning on a single measurement on compressed sensing problem, and Figures 17 show more reconstructions for the LinoSPAD and Cryo-EM datasets, respectively.
Table 7: Sell-supervised finetuning timings. Experiments on Sentinel 2 data (Tab. of main), using a single RTX 4090 GPU.
Dataset
Compressed Sensing
Demosaicing
1 image
43 sec
60 sec
10 images
80 sec
107 sec
100 images
228 sec
267 sec
Satellite images We use dataset of 200 images of 128 x 128 pixels of the coast of the United Kingdom taken by the Sentinel-2 I.IC satellite with 10-meter resolution and minimal cloud cov- erage, keeping only the RGB bands. The dataset is split into 100 images for training and 100 for testing.
Compressed sensing: we set A Sdiag(m) where m is random mask with values in {-1,+1}, and IR mxx is the discrete sine transform wit output randomly subsampled by factor of 4.
measurements are generated by applying Bayer pattern, keeping a single band per pixel. In both cases, we consider Gaussian noise with = 0.05, and finetune with the & loss for the supervised case, and with LSURE and L (using shifts) for the self- supervised setting. As shown in Table and Figure @ finetuning RAM requires only single image to obtain good results, significantly outperforming the DRUNet baselines. As shown in Table 7 self-supervised finetuning can be performed in the order of couple of minutes on a single mid-sized GPU.
Demosaicing:
Cryo electron microscopy We evaluate the model on 5 real Cryo-EM images of 7676 x 7420 pixels provided by the Topaz-EM open-source library (Bepler et al. 2020) whose noise distribution is unknown and have very low SNR. We finetune model with fixed A I, 0.98, y 0 at the input, using 256 x 256 crops normalized to have unitary standard deviation. Since the noise distribution is unknown and there is trivial nullspace, and CNULL= 0. Reconstructions are shown in Figure(6)
LSPLIT for measurement consistency
Low-photon imaging We evaluate the RAM model on the LinoSPAD data provided by Lindell ctt al. (2018), which is corrupted by photon noise. While noise in SPADs is generally assumed to be Poisson in the very low-flux case, images acquired with higher flux can follow more complex discrete noise distributions. The LinoSPAD scans the image using epipolar scanning, with some lines of pixels removed due to faulty acquisition. Thus, the image recovery problem in this is inpainting. The missing line pattern and noise model were not used during model training. We finetune the model using LSPLIT and LvoI (removing random subsets of lines). Reconstructions shown in Figure g
PDNet uDPIR-untied uDPIR-tied RAM
GFLOPS
60
2234
2234
360
Test memory (BS=1)
52
1213
298
354
Train memory (BS=8)
5815
37288
36374
9670
Table 8: Computational metries. Floating point operations (FLOPs) and memory requirements in MBytes for different methods on 256 256 color image motion deblurring.
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DDRM 5746.83 + 3.59
uDPIR tied
RAM (ours)
Denoising A I
177.08 0.23
45.21 + 0.13
Inpainting/masking A dm
5759.69 = 12.82
177.95 0.15
45.78 0.08
Blur A Fdiag k) F-1 Generic A diag m F F
5773.47 2.77
178.54 0.21
48.24 0.06
Not available
188.46 = 0.60
49.75 + 0.18
Table 9: Execution time of the proposed method. Average run time and standard deviation in milliseconds on a 512 512 color image averaged over 20 runs for different operators. Timings are reported for NVIDIA RTX 4090 GPU. Unlike DDRM and (u)DPIR, the proposed model does not require running multiple iterations, and is 3.7x faster than the unrolled counterpart uDPIR, and x20x faster than the DDRM diffusion method.
F ADDITIONAL RESULTS ON MEDICAL IMAGING TASKS
Single coil MRI We provide in Figure visual results for MRI reconstructions on acceleration factors x4 and x8 respectively.
Multi-coil MRI We provide in Figure 11 results on the multi-coil MRI inverse problem where we simulate L 15 coil maps. The UNet reconstruction shows less smooth aspect, penalizing PSNR.
y
uDPIR tied
RAM
Ground-truth
acc factor
0.863
0.868
SSIM
DCC fastor
0.831
0.840
SSIM
Figure 9: Results on MRI for acceleration factors 4 and 8. The Fourier mask is shown in the top left corner of the backprojection.
We provide in Figure 10 visual results for computed tomography for the in-distribution setup on the top row (i.e., with a setup similar to that of training), and the oft-o--distributiin setup on the bottom row. In the latter case, measurements are degraded with additional Poisson noise, unlike the training setup.
CT
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y
uDPIR tied
RAM
Ground-trut
Gaussian CT
0.607
0.642
SSIM
Poisson CT
0.828
0.671
SSIM
Figure 10: Results CT. Top CT with Gaussian noise, similar to the training setup. Bottom row: CT with Poisson noise, unseen during training.
Observed
UNet
RAM
Ground-trun
(PSNR, SSIM)
(30.7, 0.860)
(35.5, 0.937)
Figure 11: Results on the multi-coil MRI problem with acceleration factor 8. The UNet model is from (Zbontar al. 2018).
G ADDITIONAL RESULTS ON NATURAL IMAGING TASKS
Motion deblurring We provide additional visual results for the motion-blur ("hard") task on DIV2K dataset samples in Figure T2] We observe that the proposed method performs on par with uDPIR with tied weights and DDRM, although some mild visual differences may be noticed. See. for instance, the windows in the blue zoombox of the church image, which appear show a repetitive pattern in DDRM that is not faithful to the ground-truie truth image.
Super-resolution We provide visual results for bicubic super-resolution with factor and with ad- ditive Gaussian noise of standard deviation o 0.01 on BSD68 dataset samples in Fig. [3] For this task, we also compare the proposed result with the -fate-of-the-as art transformer- basedd SwinIR model (Liang et al. 2021) trained specifically for this degradation. We further compare with SR Neural Operator (SRNO) (Wei & Zhang (023). observe that the proposed method outperforms the DPIR algorithm, but underperforms compared to SwinIR and SRNO.
Grayscale Poisson-Gaussian denoising We provide visual results for Poisson- Doisson-gaussian denoising on grayscale BSD68 dataset sample in Fig. 14 We follow the formulation from On both Poisson denoising and Gaussian denoising. we observe that the model relatively robust to out-of- distribution parameters. In both cases, while the reconstruction quality degrades as the degradation level increases, cow-frequency features are preserved in the reconstruction, and no substantial arte- facts introduced.
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y
uDPIR/tied
DDRM
RAM
Ground-tre
(28.5, 0.87)
(28.4, 0.87)
(28.5, 0.87)
(32.4, 0.89)
(32.2, 0.88)
(32.4, 0.88)
(30.7, 0.89)
(31.0, 0.90)
(30.6, 0.89)
Figure 12: Deblurring results on the "Motion hard" problem, on samples from the DIV2K dataset. PSNR and SSIM metrics are provided below each reconstruction.
A T y
DPIR
SwinIR
SRNO
RAM
Ground -tuuh
(27.2, 0.78)
(28.4, 0.82)
(28.3, 0.81)
(27.6, 0.80)
(22.1, 0.65)
(23.3, 0.73)
(23.4, 0.71)
(23.0, 0.69)
(23.1, 0.63)
(23.7, 0.69)
(23.6, 0.68)
(23.4, 0.66)
Figure 13: Super-resolution results on the SRx4 problem, on samples from the BSD68 dataset. (PSNR, SSIM) metrics are provided below each reconstruction.
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Ground-truth
In-distribution
Out-of-distribttion
0.1
0.1
7 0.5
7 = 1.0
7 1.5
7 2.0
RAM output
(26.6, 0.71)
(24.2, 0.57)
(23.0, 0.53)
(22.5, 0.50)
(22.1, 0.48)
In-distribution
Out-of-distribttion
0= 0=0.01
o 0.1
0.2
0.5
σ 1.0
A
RAM output
(31.4, 0.88)
(28.9, 0.81)
(26.7, 0.71)
(22.3, 0.56)
(21.8, 0.47)
Figure 14: Grayscale Poisson-Gaussial denoising sample from the BSD68 dataset. (PSNR, SSIM) metrics are provided below each reconstruction. On the top row, we show input images and reconstruction for various y values in the
model (7). with the Gaussian component fixed ø = 0.01. On the bottom row, we show input images and reconstruction for various o values in the Poisson-Gaussian model with the Poisson component fixed 7 0.01.
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Image inpainting We provide visual results for inpainting with mild Poisson-Gaussian noise color BSD68 dataset sample in Fig. 13 We show results on inpainting masks applied either per- channel (top rows) pixel-wise (bottom rows), for various masking levels. In both cases, Poisson- Gaussian noise is applied with o = 0.01 and 7 0.01. We observe that the model is more robust to out-of-distribution mask sparsity for channel-wise masks than for pixel-wise masks, where artifacts appear when fewer than 20% of pixels are observed.
Blind deblurring Figure 18 provides additional visual results of blind deblurring using the blur identification network from Carbajal et al. (202)), on a subset of 3 real motion blur images of the Kohler dataset (Köhler et al. 2012).
Ground-truth
In-distribution
Out-of-distribttion
50%
40%
30%
20%
m 15%
V
RAM output
(31.6, 0.92)
(29.3, 0.86)
(27.9, 0.83)
(24.9, 0.76)
(21.7,0 0.68)
In-distribution
Out-of-distribttion
50%
40%
30%
20%
15%
V
RAM output
(28.0, 0.85)
(26.5, 0.82)
(24.5, 0.78)
(21.1, 0.68)
(18.2, 0.56)
Figure 15: Color inpainting with Poisson-Gaussian noise on sample from the BSD68 dataset. (PSNR, SSIM) metrics are provided below each reconstruction. Top rows: inpainting with per- channel masking, for various sparsity factors m (m = 30% means that, for each channel, 30% of the pixels are kept). Bottom rows: inpainting with pixel-wise masking, for various sparsity factors m (m 30% means that 30% of the pixels are kept).
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DRUNET zero-shat
DRUNET fineluned
RAM zero-shot
RAM finetuned
y
Figure 16: Additional LinoSPAD finetuning results.
y
DRUNET zero-shot
DRUNET finetuned
RAM zero-shol
RAM finetuned
Figure 17: Additional Cryo-EM finetuning results.
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A y
RAM
Ground -tuuh
11
IX X XI
II
II
IX
VI VII VIII VIII
IIII
VI VII
VIII
8
2
VI
Figure 18: Additional blind deblurring results on the Kohler dataset.
H UNCERTAINTY QUANTIFICATION
We can obtain uncertainty estimates of the reconstructions obtained by RAM with the equivariant bootstrap algorithm (Tachella & Pereyra 2024) described in Algorithm This approach is similar to the standard parametric bootstrap (Efron 1992), with the addition of a group of geometric trans- formations (set random shifts, rotations, and flips in our experiments). The transforms are used to probe the confidence of the network on the reconstruction in the nullspace of the operator A.
We evaluate the uncertainty quantification algorithm and Gaussian noise with standard deviation a = 0.02, using the DIV2K validation dataset. As a baseline, we use the DDRM diffusion method, running the diffusion multiple times to obtain a set of approximate posterior samples. We obtain 100 samples for each test image, which requires 101 network evaluations for equivariant bootstrap and 1000 evaluations for DDRM (since each diffusion requires 100 denoiser evaluations). Estimated pixelwise errors, averaging over all color channels, are computed using Ven of Algorithm I and shown in Figures 7 and 20 that the estimated errors follow closely the true errors.
inpainting task with 50% missing pixels
We can see in both figures
We also evaluate the accuracy of these confidence regions by calculating the empirical coverage probabilities, as measured by the proportion of ground-truth test images that lie within the con- fidence regions for a range of specified confidence levels between 0% and 100%. This method provides quantitative metric of the estimated uncertainty intervals (Thong et al.) 2024). Results are shown in Figure 19 The proposed algorithm obtains good coverage without any additional cali- bration step, while the diffusion method provides highly overconfident intervals (note this behavior of diffusion models has been observed in various previous works (Tachella & Pereyra 2024 Thong
et al. 2024
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Algorithm Equivariant Bootstrap
1: Input: Group G (. 9 acting IK via invertible maps Tg RRxx, RAM model
R. number of bootstrap samples N.
2: Reconstruct è R ( A, o, 7)
3: for ==l,..... N do
4: Draw 9i Ug)
5: Generate bootstrap measurement
ATg. 7P(
9:
+ on
with n ~ (0, I).
6:
Compute the bootstrap replicate
z T 9:
R(G'),,,,,))
0,
7: end for
8: Output: Bootstrap sample (a(1)...
x {N)}. and pixelwise errors Der
1
-x)²
N
where the squares are taken elementwise.
1.0
RAM
DDRM
Empirical coverage
0.6
0.4
0.2
0.0
0.2
0.4
0.6
1.0
Confidence level
Figure 19: Empirical coverage results. Coverage of the proposed equivariant bootstrapping with RAM and the posterior samples of DDRM (Kawar al. (2022). A good coverage should follow
the dotted line. The bootstrapping method provides significantly better uncertainty intervals than those computed using DDRM posterior samples.
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Ground truth Measurement Reconstruction
Estimated error
True error
Figure 20: Additional uncertainty quantification results. Reconstructed images from DIV2K in- painting measurements with 50% of observed pixels and Gaussian noise 0.02. The estimated emror computed using the equivariant bootstrap algorithm in Algorithm 1
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I PHASE RETRIEVAL
We consider the phase retrieval problem |Bz] where a E C a complex image, and B FFdiag(b) Fdiag(a) C is computationally efficient approximation of an iid random matrix (see e.g. Oymak et al. 2018 with F E C being the fast Fourier transform, E C and E C being phase masks with random entries of unit absolute value and random phase (sampled uniformly between O and 2T), and S E Cm and subsampling or oversampling operator.
We evaluate Algorithm on the of 100 images in the DIV2K validation set, converting them to complex values by applying the preprocessing function f(x) / æel where the exponential is computed elementwise. We use the cosine similarity as evaluation metric, since phase retrieval methods can only recover the signal up global phase (Dong et al. 2023). Figure 22 presents results for different sampling ratios m/n, showing that the proposed method can obtain robust results for m/n < 1, while standard methods only achieve good results for an oversampling ratio above m/n
n 2 4 (Dong et al. 2023), and Figure 21 shows reconstruction results on DIV2K sample in
the case m n. We use Algorithm with N 40 iterations, and noise parameters a 0.3 and
y 0.001.
A
RAM
Ground-truth
Figure 21: Phase retrieval results on a sample from the DIV2K validation set
Algorithm Phase retrieval with RAM
1: Input: Phaseless measurements y Bx Fixed y and a.
2: Initialize the estimate random á No I).
3: for v. i= 1. NN do
4: Estimate measurement phase à- = phase( Bi))
5: Reconstruct & Ry cxp(i)) B,a,7)
6: end for
7: Output: Reconstruction a
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RAM Phase Retrieval Performance
1.0
0.8
Cosine Similarity
RAM
Gradient Descent
0.4
0.2
0.0
1/16
1/8
1/4
1/2
4
Oversampling Ratio
Figure 22: Phase retrieval results for different sampling ratios. Cosine similarity obtained by Al- gorithm and gradient descent on the validation set of DIV2K images.
30


Reviews:
[{"review_id": "szU1fNWL6y", "reviewer": "Reviewer_bJqQ", "summary": "This paper proposes the Reconstruct Anything Model (RAM), a lightweight, non-iterative foundation model for computational imaging. The architecture builds on a DRUNet backbone but introduces novel components to incorporate the acquisition physics, namely a proximal estimation module for initialization and a Krylov Subspace Module (KSM) that conditions the network on iterations of the forward operator. The model is supervised trained on a wide diversity of tasks (e.g., deblurring, MRI, CT), data types (grayscale, color, complex), and noise models (Gaussian, Poisson) simultaneously.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed KSM is a clever and effective method for integrating the forward operator $A$ into a U-Net backbone without resorting to full algorithm unrolling. This, combined with the multiscale conditioning and proximal initialization, creates a powerful architecture that is well-grounded in optimization principles while remaining a fast, non-iterative network.\n\n2. The paper is exceptionally thorough. The authors provide extensive experiments covering in-distribution performance, zero-shot generalization to OOD tasks, and self-supervised finetuning.\n\n3. The results are state-of-the-art. RAM consistently outperforms strong baselines like DPIR and Restormer and performs comparably to the much larger uDPIR models, demonstrating high efficiency.", "weaknesses": "1. The related work section frames the landscape as a choice between PnP/diffusion (using denoising priors) and end-to-end/unrolled models. However, it overlooks a highly relevant, recent line of research [1-3] that proposes using restoration networks themselves as priors within iterative schemes (e.g., as fixed-point operators). These \"restoration priors\" are conceptually similar to RAM's goal (a general-purpose restoration model) but do not need specifically trained. A discussion of how RAM's end-to-end, multi-task training approach compares to these iterative restoration prior methods is necessary. \n\n2. The paper introduces multiscale operator conditioning as a key component. This is a valuable idea, but the concept has been explored before. For instance, [4] used a multiscale structure-guided approach for image deblurring. The paper would be strengthened by acknowledging this prior work and discussing the similarities or differences in its multiscale implementation.\n\n3. A core premise is that a single model can learn a universal prior for disparate imaging inverse problems. This \"one-prior-fits-all\" approach learns the best implicit prior. Does co-training on SR or deblurring tasks help with image inpainting, or does it act as a conflicting signal? The paper's own ablation in Table 4 (Right) suggests that the model trained on \"All three tasks\" (Inpainting, Deblurring, SR) performs slightly worse on inpainting and deblurring than specialist models. It t still raises the question of whether similar task-specific foundation models (e.g., \"RAM-deblur&SR\" and \"RAM-Inpainting\") might be more effective. A more detailed study of this trade-off would be valuable.\n\n\n[1] Terris, M., Kamilov, U.S. and Moreau, T., 2025. FiRe: Fixed-points of restoration priors for solving inverse problems. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 23185-23194).\n[2] Hu, Y., Peng, A., Gan, W., Milanfar, P., Delbracio, M. and Kamilov, U.S., Stochastic Deep Restoration Priors for Imaging Inverse Problems. In Forty-second International Conference on Machine Learning.\n[3] Hu, Y., Delbracio, M., Milanfar, P. and Kamilov, U.S., 2024. A RESTORATION NETWORK AS AN IMPLICIT PRIOR. In 12th International Conference on Learning Representations, ICLR 2024.\n[4] Ren, M., Delbracio, M., Talebi, H., Gerig, G. and Milanfar, P., 2023. Multiscale structure guided diffusion for image deblurring. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10721-10733).", "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": "7UppucbW97", "reviewer": "Reviewer_uPNi", "summary": "This paper introduces Reconstruct Anything Model (RAM), a non-iterative lightweight foundation model for computational imaging tasks. The model extends the DRUNet backbone with physics-aware conditioning via a Krylov Subspace Module (KSM) and a proximal initialization step. RAM aims to unify diverse inverse problems (deblurring, MRI, CT, denoising, inpainting, super-resolution) into one architecture. It further supports self-supervised finetuning without ground truth data, enabling adaptation to unseen imaging modalities. Experiments compare RAM with Plug-and-Play (DPIR), unrolled (PDNet, uDPIR), and diffusion (DDRM) methods, showing comparable or better PSNR/SSIM at 8x lower compute and 10x fewer parameters.", "questions": "1. Krylov conditioning sensitivity:\nHow stable are the learned Krylov coefficients when the forward operator $\\mathbf{A}$ is rescaled, discretized differently, or ill-conditioned? Does the model require operator-specific normalization to generalize across domains?\n\n2. Applicability beyond linear operators:\nCan RAM be extended to nonlinear or blind inverse problems (e.g., unknown blur kernels, phase retrieval), or is it fundamentally restricted to linear, known $\\mathbf{A}$?\n\n3. Effectiveness of proximal initialization:\nWhat is the concrete benefit of the proximal estimation module over simply using $\\mathbf{A}^\\top \\mathbf{y}$ or $\\mathbf{A}^+ \\mathbf{y}$? \nHave you compared their convergence behavior or training stability quantitatively?\n\n4. Self-supervised finetuning stability:\nIn cases where $\\mathbf{A}$ has a large nullspace (e.g., highly undersampled CT or MRI), how do you prevent degenerate reconstructions during self-supervised finetuning? Does the model require any regularization or early stopping?\n\n5. Runtime and efficiency evaluation:\nThe paper reports FLOPs and parameter counts, but not real-world latency. What are the actual wall-clock inference times and memory usage compared to unrolled (uDPIR) or diffusion (DDRM) baselines on 256×256 and 512×512 inputs?\n\n6. Generalization to unseen noise regimes:\nThe model claims noise-level equivariance via scale-free bias removal. Could you provide quantitative evidence that RAM maintains stable performance across unseen noise parameters $(\\sigma, \\gamma)$ beyond the training range?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper evaluates across diverse inverse problems (deblurring, MRI, CT, denoising, inpainting, super-resolution) and multiple domains (medical, microscopy, low-photon). This comprehensive experimentation is valuable for benchmarking unified restoration models.\n\n2. The proposed RAM model achieves performance comparable to unrolled and diffusion-based models while being roughly 8x faster and 10x smaller (36M parameters vs. >200M). This makes it appealing for real-time or embedded applications.\n\n3. Practical self-supervised finetuning:\nDemonstrating zero-shot and few-shot adaptation without ground truth (via SURE/UNSURE and EI/MOI losses) is practical and shows that pretrained inverse models can generalize with minimal data.\n\n4. The model is implemented cleanly with shared weights across modalities and channel configurations (color, grayscale, complex), showing solid software engineering and reproducibility.\n\n5. Despite the dense material, the manuscript maintains good structure and uses comprehensive figures (e.g., KSM illustration, self-supervised finetuning results).", "weaknesses": "1. **Limited theoretical justification:** \nKSM and proximal initialization are described intuitively, but no formal analysis of convergence, stability, or conditioning advantage is provided. This weakens the \"physics-informed\" claim.\n\n2. **Incremental contribution:**\nThe model essentially extends DRUNet with physics-based conditioning. Similar hybrid unrolled networks and foundation-style image restoration models (PromptIR, AdaIR, and MambaIR) already exist with stronger novelty or scale.\n\n3. **Overstated “foundation” claim:**\nThe term \"foundation model\" implies large-scale generalization, but training is limited to standard datasets (LSDIR, fastMRI, LIDC-IDRI)---far from the multimodal pretraining scale of foundation models in computational vision or imaging.\n\n4. Unclear fairness in comparison:\nSome baselines (e.g., DDRM) were reimplemented with reduced input resolution or adapted operators, making quantitative comparisons potentially inconsistent. No statistical significance tests are provided for small PSNR differences (~0.2--0.4 dB).\n\n5. Limited impact and novelty for ICLR:\nThe work focuses on system-level integration rather than introducing a new learning principle, theory, or large-scale paradigm. \nIts main contribution lies in engineering efficiency, that may fit better as a journal or CVPR/ICIP paper than ICLR.", "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": "CUMvnDUDl0", "reviewer": "Reviewer_sdNp", "summary": "This paper proposed reconstruction model which is universal to linear inverse problems. The authors proposed to use measurement conditioning block, conditional residual block, and Krylov subspace imposing block to achieve best performance. By utilizing mentioned components, the trained model works for various imaging tasks with one model. In addition, the model was enhanced with self-supervised finetuning exploiting equivariance to a group of transformations. The paper showed promising results.", "questions": "- The similar algorithms to solve linear inverse problems for the higher PSNRs are not easy to reconstruct severe degradation circumstances such as Figure 11 (x4). On the other hand, recent  neural operation with diffusion forward model (SRNO & diffFNO) is very strong to reconstruct details of contents with such severe conditions. What is the main advantage of using RAM instead of DiffFNO?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- author contributes to use Krylov subspace to enhance performances. This is well modeled in the signal processing perspective\n- clarity of the contents is well defined", "weaknesses": "- Worse results compared with iterative diffusion models. Not consistently winning in various settings.\n\n- Lacks of recent various algorithm including neural operator, INR, transformer etc. In particular, there are algorithms which also exploit uni-model to reconstruct several tasks such as Pre-Trained Image Processing Transformer. The authors need to include diverse benchmark algorithms. score-based approach* is more appropriate to be compared with the proposed method.\n*SOLVING INVERSE PROBLEMS IN MEDICAL IMAGING WITH SCORE-BASED GENERATIVE MODELS, ICLR22\n\n- In order to achieve Krylov space manner, this algorithm needs additional recurrent processing on forward model. This cause computational overloads. Thus, computational complexity needs to be analyzed. (i.e. computational time according to the various size of 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": {}}, {"review_id": "SGqXW26fNi", "reviewer": "Reviewer_iwVF", "summary": "This paper proposes Reconstruct Anything Model (RAM), a lightweight, non-iterative neural network designed as a foundation model for computational imaging. It directly integrates knowledge of the forward operator within a DRUNet-based architecture and is jointly trained on diverse imaging inverse problems. It is able to generalize to unseen tasks and can be finetuned self-supervisedly on new measurement data. Experimental results show strong zero-shot and fine-tuned performance across a variety of modalities, often matching or surpassing heavier iterative or unrolled baselines while being computationally efficient.", "questions": "Please see the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- RAM demonstrates generalization from a single training across multiple imaging modalities and degradation types.\n- The integration of the Krylov Subspace Module provides a new design to embed forward-operator knowledge without unrolling iterations.", "weaknesses": "- Although the paper is titled “Reconstruct Anything Model,” this claim feels overstated. The experiments are limited to a small set of linear inverse problems, while many common and practically important tasks—such as super-resolution, JPEG artifact removal, and phase retrieval—are not considered. The model also heavily relies on accurately known forward operators, which are often unknown or imperfect in real scenarios. Moreover, all evaluations are based on simulated data, so the applicability to real-world measurements remains uncertain. While I appreciate the ambition of this work, calling it “Reconstruct Anything” may mislead readers, especially for those who are new to or unfamiliar with computational imaging.\n\n- Although the architecture is newly designed, the paper lacks a clear justification for why this particular design should serve as a foundation model. The conceptual motivation and theoretical support for why the Krylov-based conditioning leads to generalization across modalities are not well explained.\n\n- It is still unclear what the advantages over deep unrolling frameworks truly are. The performance is very close to DPIR/t, which already has optimization-based interpretability and convergence guarantees. In contrast, the proposed model provides limited explanation for its empirical gains or stability properties.\n\n- The proposed multiscale operator conditioning is not clearly presented in the framework, and it seems to require explicit operations on the forward operator. However, not all forward models allow such manipulation or downscaling, which may limit the general applicability of this approach.\n\n- While the model integrates known concepts effectively, the novelty is incremental rather than fundamental. It mainly combines existing DRUNet backbones with a physics-inspired conditioning mechanism, making the contribution more of an architectural refinement than a new paradigm.", "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)