Multi-Curve Translator for High-Resolution Photorealistic Image Translation

Song, Yuda; Qian, Hui; Du, Xin

doi:10.1007/978-3-031-19784-0_8

Cited by 2 publications

(1 citation statement)

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“…Image dehazing is a type of low-level computing vision image restoration. Tang [Tang et al 2014] With the recent development of transformer network architecture, DehazeFormer [Song et al 2022] be the SOTA of publicly defogged data SOTS [Vashishth et al 2018].…”

Section: Related Work 21 Image Dehazingmentioning

confidence: 99%

Optical Aberrations Correction in Postprocessing Using Imaging Simulation

et al. 2021

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As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations. In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration. Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras. Project Page: https://github.com/TanGeeGo/ImagingSimulation .

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Section: Related Work 21 Image Dehazingmentioning

confidence: 99%