Related papers: Computational ghost imaging using deep learning
Nowadays, target recognition technique plays an important role in many fields. However, the current target image information based methods suffer from the influence of image quality and the time cost of image reconstruction. In this paper,…
X-ray based imaging modalities are widely used in research, industry, and in the medical field. Consequently, there is a strong motivation to improve their performances with respect to resolution, dose, and contrast. Ghost imaging (GI) is…
We propose PISE, a physics-informed deep ghost imaging framework for low-bandwidth edge perception. By combining adjoint operator initialization with semantic guidance, PISE improves classification accuracy by 2.57% and reduces variance by…
Techniques based on classical and quantum correlations in light beams, such as ghost imaging, allow us to overcome many limitations of conventional imaging and sensing protocols. Despite their advantages, applications of such techniques are…
Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays.…
In machine learning approach to image denoising a network is trained to recover a clean image from a noisy one. In this paper a novel structure is proposed based on training multiple specialized networks as opposed to existing structures…
The ghost imaging (GI) technique, which has attracted attention as a highly sensitive and noise-resistant technique, employs a spatially modulated illuminating light and a single-pixel detector. Generally, the information acquired by GI is…
Recently, convolutional neural networks (CNNs) have been widely used in image denoising. Existing methods benefited from residual learning and achieved high performance. Much research has been paid attention to optimizing the network…
Ghost imaging is the remarkable process where an image can be formed from photons that have not "seen" the object. Traditionally this phenomenon has required initially correlated but spatially separated photons, e.g., one to interact with…
Non-local point-to-point correlations between two photons have been used to produce "ghost" images without placing the camera towards the object. Here we theoretically demonstrated and analyzed the advantage of non-Gaussian quantum light in…
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and…
High visibility temporal ghost imaging with classical light is possible when superbunching pseudothermal light is employed. In the numerical simulation, the visibility of temporal ghost imaging with pseudothermal light equaling ($4.7\pm…
The Hadamard matrix with orthogonality is a more important modulation matrix for computational ghost imaging (CGI), especially its optimized Hadamard matrix. However, as far as we know, little mention has been paid to efficient and…
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of…
Image inpainting is an effective method to enhance distorted digital images. Different inpainting methods use the information of neighboring pixels to predict the value of missing pixels. Recently deep neural networks have been used to…
Existing frameworks for image stitching often provide visually reasonable stitchings. However, they suffer from blurry artifacts and disparities in illumination, depth level, etc. Although the recent learning-based stitchings relax such…
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the…
Deep learning methods have witnessed the great progress in image restoration with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of the restored image is relatively subjective, and it is necessary for users to control…
Detecting Graphical User Interface (GUI) elements in GUI images is a domain-specific object detection task. It supports many software engineering tasks, such as GUI animation and testing, GUI search and code generation. Existing studies for…
The capture of transient optical waveforms is critical to reveal dynamical phenomena in various fields. However, fast and sensitive mid-infrared (MIR) measurements are typically limited by processing bandwidth and detection sensitivity of…