Blind Detection and Compensation of Camera Lens Geometric Distortions

Single Image Automatic Radial Distortion Compensation Using Deep Convolutional Network

In many computer vision domains, the input images must conform with the pinhole camera model, where straight lines in the real world are projected as straight lines in the image. Performing computer vision tasks on live sports broadcast footage imposes challenging requirements where the algorithms cannot rely on a specific calibration pattern must be able to cope with unknown and uncalibrated cameras, radial distortion originating from complex television lenses, few visual clues to compensate distortion by, and the necessity for real-time performance. We present a novel method for single-image…

基于物理的深度学习用于移动光学中盲性像差恢复

Physics-consistent deep learning for blind aberration recovery in mobile optics

本研究提出Lens2Zernike框架，通过结合物理约束和多任务学习，实现从单张模糊图像中盲性恢复光学参数，提升移动摄影中反卷积的稳定性与精度。

面向盲镜头像差校正的大规模LensLib预训练与离散退化先验

Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors

提出FoundCAC框架，通过构建大规模无偏镜头库AODLibpro和离散退化先验LPR，解决数据扩展与先验缺失问题，实现盲镜头像差校正的零样本泛化和高效少样本适应。

InfoBFR：基于信息瓶颈的真实世界盲人脸恢复

InfoBFR: Real-World Blind Face Restoration via Information Bottleneck

本文提出即插即用框架InfoBFR，通过流形信息瓶颈进行信息压缩与高效扩散LoRA进行信息补偿，有效克服神经退化，在复杂场景中实现高泛化、高保真的人脸恢复。

Fast Two-step Blind Optical Aberration Correction

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image, i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens o…

Physical Cue based Depth-Sensing by Color Coding with Deaberration Network

Color-coded aperture (CCA) methods can physically measure the depth of a scene given by physical cues from a single-shot image of a monocular camera. However, they are vulnerable to actual lens aberrations in real scenes because they assume an ideal lens for simplifying algorithms. In this paper, we propose physical cue-based deep learning for CCA photography. To address actual lens aberrations, we developed a deep deaberration network (DDN) that is additionally equipped with a self-attention mechanism of position and color channels to efficiently learn the lens aberration. Furthermore, a new …