PSF Estimation Method of Simple-Lens Camera Using Normal Sinh-Arcsinh Model Based on Noise Image Pairs

Abstract

With the development of computational photography, single-lens camera combined with corresponding image deblurring algorithm is gradually becoming a new research direction, replacing complex modern optical imaging system such as single lens reflex (SLR) camera. For single-lens camera, the Point Spread Function (PSF) estimation accuracy will directly affect the image restoration effect. In this paper, we designed the simple-lens cameras with one, two and three lenses, respectively, and propose a robust and accurate PSF estimation method of simple-lens camera. The key point of estimation is to obtain the blur image and clear image pairs, which are necessary for non-blind deconvolution PSF estimation. Considering the structure characteristic of simple-lens camera, we take picture of original clear image displayed on the computer screen to get the image pairs through corner detection and color correction is made to remove color distortion. In addition, a few studies have shown that the PSF of the simple lens is close to the spatially deformed wedge, so we use a more reasonable Normal Sinh-Arcsinh (NSAS) model to fit the blur kernel and get its parameters by Powell algorithm. The experiment results have shown that the space-variant PSF estimated by the proposed method achieves better performance than the compared methods both qualitatively and quantitatively.

Research advances in simple and compact optical imaging techniques

“…The diffractive refractive hybrid imaging system. 美三片式镜头的高分辨率、高像质成像，如图 4 所示 [40] 。Zhan 等人提出了一种 鲁棒而精确的单镜头相机点扩散函数估计方法，分别设计了包含一片、两片和三 片透镜的单镜头相机，利用相机对原始清晰图像进行拍照得到模糊图像，通过角 点检测得到图像对，再利用正态 Sinh-Arcsinh 模型对空变点扩散函数进行估计， 证明了通过结合简单的透镜设计和图像去卷积方法获得高质量图像的可能性 [41] 。 Sun 等人则提出了一个基于可微分光线追迹渲染引擎的端到端复杂透镜的设计框 架，通过所设计的模型可以对特定成像任务来联合优化镜头模块和图像重建网络， 并在大视场成像和扩展景深成像的典型应用中实现了优于传统透镜设计的图像 质量 [42] 。Tseng 等人针对现有复杂光学设计与图像处理或分析任务分离的问题， 提出了联合优化方法，建立一种学习可微的复合光学正向演化模型及一种交替的 近端优化方法，同时实现光学设计工具 Zemax 的无缝集成，在车载相机上实现了 物体或交通信号灯状态等目标的高质量检测识别 [43] 。Ji 等人针对视场和简单图像 退化模型对计算成像技术的限制，提出了一种用于大视场成像、低成本的计算成 像方法 [44] 。所设计的系统由一个带双球面透镜的广角光学模块和一个用于图像重 建的深度学习网络组成，为提高图像质量对简单光学模块引入了加权斑块退化模 型，并创建了 DMPH-SE 网络，在可见光波段和 64°宽视场内实现了理想的成像 效果。 图4 简单光学系统设计结构图和复原结果比较 [40] [30] 2 单透镜 估计单透镜 PSF 函数， 再基于交叉通道先验执行非盲去卷 积算法复原图像 Heide 等人 [31] 3 单透镜 使用快速可微光线追迹模型和基于 Res-Unet 的恢复网络 实现单镜头端到端设计和高质量成像 Li 等人 [34] 4 折衍混合透镜 由可微分光学层的深度相关 PSF 对全聚焦图像进行编码， 再利用基于 U-Net 的深度网络对编码图像进行深度图重构…”

“…Zhan 等人 [41] 7 外波段达到衍射极限性能 [46] 。Chen等人将视场离轴与孔径离轴相结合，利用自由 曲面校正系统像差，在8~12 μm长波红外实现了视场角为8°×5°、F数为2的成像系…”

Single Radar Image Motion Deblurring Through PSF Estimation and Convolution Regularizer

“…Accurate estimation of the PSF is essential for motion deblurring, as it helps to deconvolve the image and restore the lost image details and resolution. PSF estimation algorithms typically estimate the two motion parameters that define the PSF, namely the blur angle and the blur length [15], [16]. Once these parameters are estimated, the PSF can be constructed and used to deconvolve the image.…”