In this paper, we evaluate the noise sensitivity of mask based lensless imaging by analysing how much information is lost due to camera noise in low-light conditions. A noise model based on a lensless imaging system under lowlight conditions is presented for noise analysis in lensless imaging. The structure utilises the noise-free point spread function (PSF) of a diffuser-based lensless system to generate a noisy PSF from a camera in a real-world low-light environment. We then use a statistical approach to observe how the amount of mutual information between the noiseless and the noisy PSF’s is affected with the enhancement of camera noise. We performed the same simulation for comparison with lens-based imaging. We demonstrate that the proposed model and the mask based lensless imaging system exhibits better robustness than the lens-based imaging system in low-light environments.
This paper introduces a deep learning-based low-light lensless image reconstruction enhancement algorithm that can effectively reconstruct low-light lensless images and achieve significant enhancement effects under low-light conditions. The algorithm comprises two stages: (1) A preliminary imaging stage, where the forward imaging model is utilized as a prior knowledge to obtain the initial reconstruction results; (2) A perceptual enhancement network built upon the conditional diffusion model for detailed enhancement, resulting in realistic images with normal lighting and reduced noise. Experimental results on simulated datasets demonstrate that this algorithm exhibits superior reconstruction performance with Learned Perceptual Image Patch Similarity (LPIPS) and Structural Similarity Index Measure (SSIM) up to 0.0887 and 0.7454, respectively, and successfully realizes the reconstruction and enhancement of lensless low-light images.
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