Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works only focus on improving the subjective imaging quality through computational optics, i.e. Computational Imaging (CI) technique, ignoring the feasibility in semantic segmentation. In this paper, we pioneer to investigate Semantic Segmentation under Optical Aberrations (SSOA) of MOS. To benchmark SSOA, we construct Virtual Prototype Lens (VPL) groups through optical simulation, generating Cityscapes-ab and KITTI-360-ab datasets under different behaviors and levels of aberrations. We look into SSOA via an unsupervised domain adaptation perspective to address the scarcity of labeled aberration data in real-world scenarios. Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA. Based on our benchmark, we conduct experiments on the robustness of stateof-the-art segmenters against aberrations. In addition, extensive evaluations of possible solutions to SSOA reveal that CIADA achieves superior performance under all aberration distributions, paving the way for the applications of MOS in semantic scene understanding.1

https://export.arxiv.org/pdf/2211.11257v1.pdf

Computational Optics Meet Domain Adaptation: Transferring Semantic Segmentation Beyond Aberrations

Computational Imaging for Machine Perception: Transferring Semantic Segmentation Beyond Aberrations

Design and Implementation of Radant Lens Test

We proposed a digital-optical co-design that can effectively improve the image quality of refractive-diffractive hybrid imaging systems over a wide ambient temperature range. Diffraction theory was used to establish the degradation model and blind deconvolution image recovery algorithm was used to perform recovery for simulated images. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were used to evaluate the algorithm performance. A cooled athermalization dual-band infrared optical system with a double-layer diffractive optical element (DLDOE) was designed, and the results show an overall improvement for both PSNR and SSIM overall the whole ambient temperature range. This demonstrates the effectiveness of the proposed method for the image quality improvement of hybrid optical systems.

Digital-optical co-design enables athermalization of hybrid optical systems.

Diffractive optical elements (DOEs) are difficult to apply in natural scenes imaging covering the visible bandwidth-spectral due to their strong chromatic aberration and the decrease in diffraction efficiency. Advances in computational imaging make it possible. In this paper, the image quality degradation model of DOE in bandwidth-spectral imaging is established to quantitatively analyze its degradation process. We design a DDZMR network for a single-lens diffractive lens computational imaging system, which can simultaneously perform image restoration and image super-resolution reconstruction on degraded images. The multimodal loss function was created to evaluate the reconstruction of the diffraction imaging degradation by the DDZMR network. The prototype physical prototype of the single-lens harmonic diffraction computational imaging system (SHDCIS) was built to verify the imaging performance. SHDCIS testing showed that optical chromatic aberration is corrected by computational reconstruction, and the computational imaging module can interpret an image and restore it at 1.4 times the resolution. We also evaluated the performance of the DDZMR model using the B100 and Urban100 datasets. Mean Peak Signal to Noise Ratio (PSNR)/Structural Similarity (SSIM) were, respectively, 32.09/0.8975 and 31.82/0.9247, which indicates that DDZMR performed comparably to the state-of-the-art (SOTA) methods. This work can promote the development and application of diffractive imaging systems in the imaging of natural scenes in the bandwidth-spectrum.

/pdf/computational-imaging-for-simultaneous-image-restoration-and-22hgqrgm.pdf

Computational Imaging for Simultaneous Image Restoration and Super-Resolution Image Reconstruction of Single-Lens Diffractive Optical System

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Computational Imaging for Simultaneous Image Restoration and Super-Resolution Image Reconstruction of Single-Lens Diffractive Optical System