arXiv subjects

Partha Das

Publications and source records attributed to Partha Das.

13 recordsLinked to original sources

arXiv2025

A comparative study of focusing with scalar and vector beams in an active Raman gain system

We investigate the focusing characteristics of scalar and vector beams within an atomic medium. An active-Raman-gain configuration is employed to achieve significant Kerr nonlinearity in a four-state atomic system. The probe beams can attain focusing within the medium through careful selection of input beam intensities and the spatial profile of the control field. We analytically derive the linear and third-order nonlinear susceptibilities for both scalar and vector probe beams. Our observations indicate that, in addition to the energy transfer from the control beam to the probe beam, the giant cross-Kerr nonlinearity facilitates the focusing of the scalar probe beam into a significantly smaller spot size. Conversely, the vector probe beams exhibit gain-induced narrowing. Furthermore, we evaluate the state of polarization for the vector beam at the minimum beam waist, observing a polarization rotation and a change in ellipticity during propagation. Through the mechanism of focusing, we achieve a reduced spot size for the probe beam, which may have substantial implications for resolution enhancement in microscopy applications.

physics.optics↗

arXiv2024

Linear and nonlinear propagation of cylindrical vector beam through a non-degenerate four level atomic system

We investigate the phase-induced susceptibilities for both components of the probe vector beam (PVB) within an atomic system. The atoms are prepared in a non-degenerate four-level configuration. The transitions are coupled by a $\pi$ polarized control field and two orthogonally polarized components of a PVB. We show that the linear susceptibility of the medium depends on the phase shift between the control field and PVB, characterizing loss or gain in the system. Additionally, the phase shift causes polarization rotation in the vector beams (VBs) as they propagate. We further study the effect of nonlinearity on the VB propagation through the medium for a couple of Rayleigh lengths. The self-focusing and defocusing phenomena are observed for radial, azimuthal, and spiral VBs. The special chain-like self-focusing and defocusing leads to the formation of consecutive smaller spot sizes with moderate gain. Therefore, the mechanism of control of susceptibility and self-focusing may hold promise for applications such as transitioning from an absorber to an amplifier, high-resolution microscopy, and optical trap systems.

physics.optics↗

arXiv2023

Structured beam controlled super-resolution in quantum dots via rapid adiabatic passage

We theoretically investigate rapid adiabatic passage (RAP) based super-resolution microscopy in a two-level quantum dot (QD) system. The system consists of a QD interacting with two structured beams, accompanied by chirping and a time delay. The central concept of this work is inspired by the stimulated emission depletion (STED) microscopy technique. To understand the physical mechanism behind super-resolved spot formation, we employ a variational master equation for the density matrix, incorporating both radiative and non-radiative decay processes. A suitably chosen spatiotemporal envelope of the structured beams enables the formation of a super-resolved image. Unwanted low-intensity circular rings around the focal spot are suppressed using Bessel-modulated truncated structured Laguerre-Gaussian (LG) and super-Gaussian (SG) beams. We also study the temperature dependence of the imaging scheme. The numerical results confirm that at low pulse areas, exciton-phonon coupling distorts the image, whereas at higher pulse areas, exciton-phonon decoupling preserves the image resolution. Hence, the proposed scheme may open up new possibilities for nanoscale imaging and bioimaging applications using QDs.

cond-mat.mes-hall↗

arXiv2022

SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes

Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to not being able to include all possible imaging conditions). In this paper, a combination of the two is proposed. Component specific priors like semantics and invariant features are exploited to obtain semantically and physically plausible reflectance transitions. These transitions are used to steer a progressive CNN with implicit homogeneity constraints to decompose reflectance and shading maps. An ablation study is conducted showing that the use of the proposed priors and progressive CNN increase the IID performance. State of the art performance on both our proposed dataset and the standard real-world IIW dataset shows the effectiveness of the proposed method. Code is made available at https://github.com/Morpheus3000/SIGNet

cs.CV↗

arXiv2022

PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition

Intrinsic image decomposition is the process of recovering the image formation components (reflectance and shading) from an image. Previous methods employ either explicit priors to constrain the problem or implicit constraints as formulated by their losses (deep learning). These methods can be negatively influenced by strong illumination conditions causing shading-reflectance leakages. Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is proposed for intrinsic image decomposition. Edges correspond to illumination invariant gradients. To handle hard negative illumination transitions, a hierarchical approach is taken including global and local refinement layers. We make use of attention layers to further strengthen the learning process. An extensive ablation study and large scale experiments are conducted showing that it is beneficial for edge-driven hybrid IID networks to make use of illumination invariant descriptors and that separating global and local cues helps in improving the performance of the network. Finally, it is shown that the proposed method obtains state of the art performance and is able to generalise well to real world images. The project page with pretrained models, finetuned models and network code can be found at https://ivi.fnwi.uva.nl/cv/pienet/.

cs.CV↗

arXiv2022

Pulse amplification in a closed loop $\Lambda$ system with permanent dipole moments

Propagation of a weak Gaussian probe pulse through a closed loop $\Lambda$ system with permanent dipole moments (PDMs) is investigated in presence of a strong control field along with a third field. The presence of PDMs allows multi photon excitation, which are otherwise forbidden. The PDMs modify the Rabi frequencies of the probe, control, and the third field inside the medium which noticeably affects the propagation of probe pulse. The probe pulse is amplified during propagation with its Gaussian shape intact. Due to unprohibited two photon excitation it is possible to amplify a probe pulse whose frequency is twice of the control field's frequency, with the help of the third field.

physics.optics↗

arXiv2021

Generative Models for Multi-Illumination Color Constancy

In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color constancy method. GANs are exploited to model the illumination estimation problem as an image-to-image domain translation problem. Additionally, a novel multi-illumination data augmentation method is proposed. Experiments on single and multi-illumination datasets show that our methods outperform sota methods.

cs.CV↗

arXiv2021

ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

cs.CV↗

arXiv2020

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at https://lhoangan.github.io/eden.

cs.CV↗

arXiv2020

Novel View Synthesis from Single Images via Point Cloud Transformation

In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired. Our method estimates point clouds to capture the geometry of the object, which can be freely rotated into the desired view and then projected into a new image. This image, however, is sparse by nature and hence this coarse view is used as the input of an image completion network to obtain the dense target view. The point cloud is obtained using the predicted pixel-wise depth map, estimated from a single RGB input image,combined with the camera intrinsics. By using forward warping and backward warpingbetween the input view and the target view, the network can be trained end-to-end without supervision on depth. The benefit of using point clouds as an explicit 3D shape for novel view synthesis is experimentally validated on the 3D ShapeNet benchmark. Source code and data will be available at https://lhoangan.github.io/pc4novis/.

cs.CV↗

arXiv2018

Color Constancy by GANs: An Experimental Survey

In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experimental survey is provided on the use of different types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs). Based on the experimental review, recommendations are given for the design of CC-GAN architectures based on different criteria, circumstances and datasets.

cs.CV↗

arXiv2018

Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinseg

cs.CV↗

arXiv2017

Development of Diagnostics for High-Temperature High-Pressure Liquid Pb-16Li Applications

Liquid lead-lithium (Pb-16Li) is of primary interest as one of the candidate materials for tritium breeder, neutron multiplier and coolant fluid in liquid metal blanket concepts relevant to fusion power plants. For an effective and reliable operation of such high temperature liquid metal systems, monitoring and control of critical process parameters is essential. However, limited operational experience coupled with high temperature operating conditions and corrosive nature of Pb-16Li severely limits application of commercially available diagnostic tools. This paper illustrates indigenous calibration test facility designs and experimental methods used to develop non-contact configuration level diagnostics using pulse radar level sensor, wetted configuration pressure diagnostics using diaphragm seal type pressure sensor and bulk temperature diagnostics with temperature profiling for high temperature, high pressure liquid Pb and Pb-16Li applications. Calibration check of these sensors was performed using analytical methods, at temperature between 380C-400C and pressure upto 1 MPa (g). Reliability and performance validation were achieved through long duration testing of sensors in liquid Pb and liquid Pb-16Li environment for over 1000 hour. Estimated deviation for radar level sensor lies within [-3.36 mm, +13.64 mm] and the estimated error for pressure sensor lies within 1.1% of calibrated span over the entire test duration. Results obtained and critical observations from these tests are presented in this paper.

physics.ins-det↗