Integrated Forward-Inverse Network for Lensless Image Reconstruction

Accepted to ECCV 2026

Donggeon Bae¹, Jaewoo Jung^2,3, Yong Guk Kang², Kyung Chul Lee⁴, Taeyoung Kim³, Jongho Kim¹, Sangjun Byun¹, Joonsik Park³, and Seung Ah Lee^1,2

¹Seoul National University, Department of Mechanical Engineering
²Seoul National University, School of Mechanical and Aerospace Engineering/SNU-IAMD
³Yonsei University, Department of Electrical and Electronic Engineering
⁴University of Michigan, Department of Biomedical Engineering

Abstract

Lensless imaging enables compact and versatile computational cameras by replacing bulky optics with thin coded elements. However, reconstruction from the resulting measurements is challenging: large-footprint PSFs produce highly multiplexed observations, making inversion severely ill-conditioned and sensitive to calibration errors and model mismatch. While deep learning approaches, including hybrid models that incorporate physics priors, have shown promise, explicitly maintaining data fidelity throughout the network hierarchy remains difficult. Here, we propose the Integrated Forward-Inverse Network (IFIN), a physics-guided architecture that interleaves differentiable forward projections with learnable inverse updates at every stage, enabling complementary cues to be exploited jointly in the measurement and image domains. This bidirectional coupling supports progressive, physics-consistent refinement and permits system-constrained PSF kernel adaptation under model uncertainty. On challenging lensless benchmarks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performance on Gaussian deblurring and simulated inline holography reconstruction, suggesting that the same interleaving principle can extend beyond lensless cameras.

Method

IFIN uses an encoder-decoder backbone with Integrated Forward-Inverse Blocks (IFIBs) at every scale. Each IFIB exchanges information between an image-domain stream and a measurement-domain stream using:

• Forward System Operator (FSO): projects the current image-domain representation through the optical forward model.
• Inverse System Operator (ISO): applies a learnable Wiener-like inverse update from the measurement domain.
• Learnable PSF field: refines system kernels end-to-end for calibration mismatch and shift-variant degradation.

Rather than applying a single inversion and then relying only on learned refinement, IFIN repeatedly exchanges forward projections and inverse updates throughout the feature hierarchy. This keeps measurement-domain evidence available during reconstruction.

_{Overall architecture of IFIN. IFIBs are inserted at each encoder-decoder scale to jointly apply FSO and ISO with a shared learnable PSF field.}

_{Integrated Forward-Inverse Block. IFIN couples forward projection and inverse restoration in either a single-PSF or PSF-field setting.}

Results

IFIN improves reconstruction quality across three lensless benchmarks: DiffuserCam, WiderCam, and the MultiWienerNet dataset.

DiffuserCam

Method	PSNR	LPIPS	SSIM
ADMM	12.252	0.607	0.346
Wiener Deconv.	12.552	0.591	0.384
ISO (Ours)	16.528	0.544	0.404
UNet	21.230	0.394	0.656
NAFNet	24.830	0.239	0.810
Le-ADMM-U	23.261	0.312	0.765
DeepLIR	25.958	0.260	0.829
MWNet	24.832	0.247	0.810
UPDN	28.228	0.194	0.877
MWDNs	27.298	0.217	0.845
LensNet	27.650	0.201	0.868
MoDL	27.958	0.183	0.878
IFIN (Ours)	29.862	0.174	0.893

WiderCam

Method	PSNR	LPIPS	SSIM
ADMM	11.843	0.643	0.323
Wiener Deconv.	12.405	0.607	0.369
ISO (Ours)	17.240	0.462	0.444
UNet	21.890	0.474	0.646
NAFNet	23.857	0.245	0.769
Le-ADMM-U	21.956	0.278	0.748
DeepLIR	20.523	0.339	0.642
MWNet	23.001	0.255	0.766
UPDN	23.920	0.229	0.801
MWDNs	24.525	0.224	0.801
LensNet	24.615	0.219	0.806
MoDL	24.791	0.202	0.810
IFIN (Ours)	25.444	0.201	0.824

MultiWienerNet

Method	PSNR	LPIPS	SSIM
ADMM	19.189	0.557	0.420
Wiener Deconv.	18.658	0.640	0.302
ISO (Ours)	20.202	0.623	0.380
UNet	23.859	0.389	0.589
NAFNet	24.657	0.282	0.712
Le-ADMM-U	23.732	0.335	0.702
DeepLIR	22.556	0.379	0.642
MWNet	25.660	0.260	0.728
UPDN	24.364	0.287	0.707
MWDNs	27.436	0.236	0.780
LensNet	27.546	0.221	0.809
MoDL	28.504	0.202	0.831
IFIN (Ours)	31.083	0.175	0.866

_{Visual comparison on DiffuserCam display-capture data. IFIN preserves color fidelity and high-frequency textures while suppressing artifacts.}

_{Comparison on WiderCam. IFIN mitigates field-dependent peripheral blur and geometric distortion while preserving fine textures and edges.}

_{Comparison on MWNet. Results include simulated spatially variant measurements and experimental miniscope captures.}

More qualitative results and system details are available on the project page.

Inline Holography Reconstruction

IFIN also transfers to simulated inline holography reconstruction by replacing the system operators with angular-spectrum forward propagation and corresponding back-propagation.

Method	PSNR	LPIPS	SSIM
RCAN	23.764	0.354	0.702
NAFNet	23.224	0.389	0.634
NAFNet+	25.751	0.242	0.817
IFIN (Ours)	28.302	0.166	0.890

_{Inline holography reconstruction. IFIN adapts to the holographic forward model and recovers cleaner object structures from a single intensity hologram.}

Installation

git clone https://github.com/IIL-SNU/IFIN.git
cd IFIN
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Quick Start

The default configuration follows the DiffuserCam/Waller-style paired dataset layout. For a dependency and code-path smoke test without external data, switch data.dataset in configs/default.yaml to synthetic.

Train:

python train.py --config configs/default.yaml

Evaluate:

python eval.py --config configs/default.yaml --checkpoint outputs/checkpoints/ifin_last.pth

Run inference:

python infer.py --config configs/default.yaml --checkpoint outputs/checkpoints/ifin_last.pth

Run tests:

python -m pytest -q tests

Dataset Layout

For DiffuserCam/Waller-style data, set data.waller_path and data.psf_path in configs/default.yaml.

Expected layout:

dataset_root/
  dataset_train.csv
  dataset_test.csv
  diffuser_images/
  ground_truth_lensed/
  psf.tiff

WiderCam Dataset

We introduce WiderCam, a wide-field lensless reconstruction benchmark captured with a compact phase-mask camera. The dataset contains 25,000 paired measurements, split into 24,000 training and 1,000 test images, with strong field-dependent degradation over a wide field of view. Raw measurements are captured at 4608 x 2592 using a Sony IMX708 sensor and resized to 480 x 270; supervision is affine-aligned offline from the display-capture pair.

Dataset: Dataset page

Citation

@inproceedings{bae2026ifin,
  title     = {Integrated Forward-Inverse Network for Lensless Image Reconstruction},
  author    = {Bae, Donggeon and Jung, Jaewoo and Kang, Yong Guk and Lee, Kyung Chul and Kim, Taeyoung and Kim, Jongho and Byun, Sangjun and Park, Joonsik and Lee, Seung Ah},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

Contact

For access to additional datasets, checkpoints, or code, please contact donggeonbae@snu.ac.kr.