PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging

1The Chinese University of Hong Kong, 2Shanghai AI Laboratory,
3Tsinghua University, 4SenseTime

Lensless cameras offer significant advantages in size, weight, and cost compared to traditional lens-based systems. Without a focusing lens, lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, current algorithms struggle with inaccurate forward imaging models and insufficient priors to reconstruct high-quality images. To overcome these limitations, we introduce a novel two-stage approach for consistent and photorealistic lensless image reconstruction. The first stage of our approach ensures data consistency by focusing on accurately reconstructing the low-frequency content with a spatially varying deconvolution method that adjusts to changes in the Point Spread Function (PSF) across the camera's field of view. The second stage enhances photorealism by incorporating a generative prior from pre-trained diffusion models. By conditioning on the low-frequency content retrieved in the first stage, the diffusion model effectively reconstructs the high-frequency details that are typically lost in the lensless imaging process, while also maintaining image fidelity. Our method achieves a superior balance between data fidelity and visual quality compared to existing methods, as demonstrated with two popular lensless systems, PhlatCam and DiffuserCam.

Comparison of lensless imaging approaches

We introduce PhoCoLens, a lensless reconstruction algorithm that achieves both better visual quality and consistency to the ground truth than existing methods. Our method recovers more details compared to traditional reconstruction algorithms (b) and (c), and also maintains better fidelity to the ground truth compared to the generative approach (d).

Overview of PhoCoLens Pipeline

The two-stage pipeline begins with a spatially varying deconvolution network mapping lensless measurements to range space. Then a conditional diffusion model for null space recovery refines details using the first stage output, achieving the final reconstruction.

Qualitative Results

Qualitative comparison between our method and others on the PhlatCam dataset.

Qualitative comparison between our method and others on the DiffuserCam dataset.

Quantitative Results

Quantitative comparison between our method and others on the PhlatCam dataset.