Intensity diffraction tomography (IDT) encompasses a class of optical microscopy techniques designed to reconstruct the three-dimensional refractive index (RI) distribution of a sample from a series of two-dimensional intensity-only measurements. However, reconstructing artifact-free RI maps remains a fundamental challenge in IDT owing to the loss of phase information and the missing-cone problem. Neural fields (NF), a deep learning framework increasingly adopted in computer vision and graphics, offer a promising approach by using a neural network to map 3D spatial coordinates to optical properties—such as color, opacity, and refractive index. we propose a method called Intensity Diffraction Tomographic Compressed Imaging based on Residual Neural Fields (IDTCI-RNF). Our approach integrates a compressed sampling mechanism into the IDT imaging model and employs a self-supervised deep learning framework enhanced with 3D Total Variation (3DTV) regularization to preserve edge details. The proposed method enables high-quality reconstruction of 3D refractive index distributions from low-dimensional discrete intensity measurements. Experimental results demonstrate that IDTCI-RNF produces high-contrast, artifact-free RI maps and outperforms existing methods in retaining structural edges and fine details.
A new optical single-channel security system based on Schur decomposition, biometric random phase encoding in fractional Fourier transform domains is proposed. In this technique, the grayscale iris image is decomposed by Schur decomposition. Unitary matrix U and upper triangular matrix T are encoded and modulated independently by a random phase mask to produce a biometric random phase mask (BRPM). An original color image is split into R-, G- and B- channels. Each channel is decomposed by Schur decomposition. The unitary matrix U. of each channel is combined to construct a single-channel image as an input image. The upper triangular matrix T of each channel is utilized as decryption key. A single-channel image is modulated with U- BRPM and then fractional Fourier transformed. The fractional Fourier spectrum is amplitude- and phase-truncated to obtain first encrypted image and first decryption key. The first encrypted image is modulated with T- BRPM and then fractional Fourier transformed. The fractional Fourier spectrum is amplitude- and phase-truncated to get final encrypted image and second decryption key. The proposed cryptosystem has three main advantages. First, the proposed system has four decryption keys to evade potential attacks. Second, U- BRPM and T- BRPM are utilized as unique encryption keys to resist unauthorized access. Finally, the fractional orders of the fractional Fourier transform provide sensitive encryption keys. The proposed method can be executed using a hybrid optoelectronic system. The viability, efficiency, and security of the proposed system can be shown by numerical simulation results.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：