Lensless (Diffuser Camera) Imaging
Lensless imaging replaces the objective lens with a thin optical element (phase diffuser or coded mask) placed directly near the sensor. Scene light produces a multiplexed caustic pattern encoding the entire scene. The forward model is y = H * x + n where H is determined by the mask's phase profile and mask-to-sensor distance. Each scene point contributes across many sensor pixels, yielding a multiplexing advantage. Reconstruction solves a large-scale inverse problem via ADMM or FISTA with total-variation or learned priors.
Psf Convolution Or Linear Operator
Poisson Gaussian
admm tv
CMOS
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
P(diffuser) → D(g, η₁)
Benchmark Variants & Leaderboards
Lensless
Lensless (Diffuser Camera) Imaging
P(diffuser) → D(g, η₁)
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | Uformer | 0.768 | 33.5 | 0.920 | ✓ Certified | Wang et al., CVPR 2022 |
| 🥈 | FlatNet | 0.725 | 31.8 | 0.890 | ✓ Certified | Khan et al., IEEE TPAMI 2020 |
| 🥉 | PnP-ADMM | 0.603 | 27.5 | 0.790 | ✓ Certified | Monakhova et al., Opt. Express 2019 |
| 4 | Wiener-ADMM | 0.462 | 23.5 | 0.640 | ✓ Certified | Antipa et al., Optica 2018 |
Mismatch Parameters (3) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| diffuser_psf | ΔPSF | Diffuser PSF calibration error (%) | 0 | 5.0 |
| sensor_distance | Δd | Diffuser-sensor distance error (mm) | 0 | 0.2 |
| wavelength | Δλ | Wavelength mismatch (nm) | 0 | 5.0 |
Reconstruction Triad Diagnostics
The three diagnostic gates (G1, G2, G3) characterize how reconstruction quality degrades under different error sources. Each bar shows the relative attribution.
Model: psf convolution or linear operator — Mismatch modes: psf calibration error, mask sensor misalignment, depth dependent psf variation, stray light
Noise: poisson gaussian — Typical SNR: 10.0–30.0 dB
Requires: psf measurement, mask to sensor distance, flatfield, background subtraction
Modality Deep Dive
Principle
Lensless (diffuser-cam) imaging replaces the imaging lens with a thin diffuser or coded mask placed directly before the sensor. The sensor records a multiplexed pattern (caustic or speckle) that encodes the 3-D scene. Computational reconstruction inverts the known point-spread function of the diffuser to recover the image, enabling an extremely compact, lightweight camera suitable for miniaturized or in-vivo applications.
How to Build the System
Place a thin diffuser (ground glass, engineered phase mask, or Scotch tape) at a fixed, small distance (~1-5 mm) from a bare sensor (CMOS, e.g., Sony IMX sensor). Precisely characterize the diffuser PSF by scanning a point source across the field of view. Mount rigidly to prevent any relative motion between diffuser and sensor. For 3-D reconstruction, the depth-dependent PSF must be calibrated at multiple axial planes.
Common Reconstruction Algorithms
- ADMM (alternating direction method of multipliers) with TV regularization
- Wiener deconvolution (fast, single-step but lower quality)
- Gradient descent with learned priors (DiffuserCam, neural network prior)
- Tikhonov-regularized least squares
- Unrolled optimization networks (physics-informed deep learning)
Common Mistakes
- Inaccurate PSF calibration causing reconstruction artifacts
- Insufficient sensor dynamic range for the caustic intensity peaks
- Motion between diffuser and sensor during capture invalidating the PSF model
- Regularization too strong, over-smoothing fine details in the reconstruction
- Ignoring the depth-dependence of the PSF when imaging 3-D scenes
How to Avoid Mistakes
- Calibrate PSF carefully with a point source at the exact sample distance
- Use HDR acquisition or high-bit-depth sensors to capture full caustic range
- Rigidly bond the diffuser to the sensor; verify alignment stability
- Tune regularization weight (e.g., via L-curve or cross-validation)
- Calibrate PSF at multiple depths for 3-D scenes; use depth-varying reconstruction
Forward-Model Mismatch Cases
- The widefield fallback uses a Gaussian PSF, but lensless cameras use a coded aperture (phase mask, diffuser, or amplitude mask) that creates a highly structured, non-Gaussian PSF — the caustic pattern is fundamentally different from a Gaussian
- The lensless PSF encodes the scene through a known, shift-variant pattern — the widefield shift-invariant Gaussian blur does not capture the scene-dependent structure of the lensless measurement and produces incorrect reconstruction input
How to Correct the Mismatch
- Use the lensless operator with the calibrated PSF of the specific coded aperture (measured from a point source or computed from the mask design): y = H * x, where H is the non-Gaussian, possibly shift-variant PSF
- Reconstruct using Wiener deconvolution, ADMM with TV prior, or learned methods (FlatNet, PhlatCam) that use the correct coded-aperture PSF for the specific mask in use
Experimental Setup
DiffuserCam / FlatCam prototype
Raspberry Pi HQ Camera (Sony IMX477, 4056x3040)
1.55
optical diffuser (Luminit 0.5-deg) / coded mask
2.5
40
2592x1944
Signal Chain Diagram
Key References
- Antipa et al., 'DiffuserCam: lensless single-exposure 3D imaging', Optica 5, 1-9 (2018)
- Asif et al., 'FlatCam: Thin, Lensless Cameras Using Coded Aperture', IEEE TCI 3, 384-397 (2017)
Canonical Datasets
- DiffuserCam lensless mirflickr dataset (Monakhova et al.)
- PhlatCam benchmark (Boominathan et al., IEEE TPAMI 2022)