Gradient Based Optimization Of Time Multiplexed Binary Computer

A Gradient Based Optimization Algorithm For Lasso Pdf We propose a gradient based optimization method for time multiplexed binary holograms displayed on a digital mirror device. optimized binary holograms can be used to reconstruct high quality images. In this research, we utilize the binary amplitude spatial light modulator (slm), which has a high refresh rate such as digital mirror device (dmd), to optimize the hologram in time multiplexing to display a high definition reproduced image.

Gradient Based Optimization Of Time Multiplexed Binary Computer We implemented this method both to directly optimize binary holograms, and to train deep learning based cgh models. simulations and experimental results show that our method achieves greater speed, and higher accuracy and contrast than existing algorithms. In this paper, we proposed an optimization algorithm based on conjugate gradient, which is distinct from the widely used cgh generation algorithms such as sgd and wh. This work builds up on the idea of time averaging multiple hologram frames, first introduced in methods like one step phase retrieval and adaptive one step phase retrieval. The gerchberg–saxton (gs) algorithm is a fourier iterative algorithm that can effectively optimize phase only computer generated holograms (cghs). this study proposes a new optimization technique for phase only cghs based on the gradient descent method.

Gradient Based Optimization Of Time Multiplexed Binary Computer This work builds up on the idea of time averaging multiple hologram frames, first introduced in methods like one step phase retrieval and adaptive one step phase retrieval. The gerchberg–saxton (gs) algorithm is a fourier iterative algorithm that can effectively optimize phase only computer generated holograms (cghs). this study proposes a new optimization technique for phase only cghs based on the gradient descent method. We explore surrogate gradient methods for optimizing the heavily quantized slm patterns of emerging mems based phase slms and show the gumbel softmax algorithm to outperform other approaches. This paper introduces a novel gradient based approach for multilevel optimization designed to overcome these limitations by leveraging a hierarchically structured decomposition of the full gradient and employing advanced propagation techniques.