arXiv subjects

Honey Gupta

Publications and source records attributed to Honey Gupta.

5 recordsLinked to original sources

arXiv2026

Shaping SHAPE - A spectro-polarimeter onboard Chandrayaan-3 to observe Earth as an Exoplanet

Spectro-polarimetry of HAbitable Planet Earth (SHAPE) is an experimental instrument onboard the Propulsion Module (Orbiter) of the Chandrayaan-3 mission, designed to perform disc-integrated spectro-polarimetric observations of Earth from lunar and highly elliptical Earth orbits. SHAPE is a compact, lightweight spectro-polarimeter comprising three subsystems: the Electro-Optical Detector System (EODS)-Optics, EODS-Electronics, and Radio Frequency Source (RFS). An Acousto-Optic Tunable Filter (AOTF), driven by an in-house-developed 80$-$135 MHz RF source, provides spectral filtering in the near-infrared (NIR) wavelength range of 1.0$-$1.7 $\mu$m and produces two narrow-band beams with mutually perpendicular linear polarization states. The instrument optics, with a field of view of approximately 2.6{\deg}, focus the two beams onto InGaAs detectors. A spectral resolution of 2$-$4 nm is achieved using in-house-designed low-noise front-end electronics. The instrument also incorporates processing and power electronics for signal processing, detector biasing, and subsystem control. We present the overall instrument design, results from pre-launch ground-based testing, and in-orbit operational performance. The current configuration enables SHAPE to measure disc-integrated signatures of Earth over a range of phase angles, providing a test bed for characterizing Earth-like exoplanets and benchmarking future exoplanet observations.

astro-ph.IM↗

arXiv2026

When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use

The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings. A common failure occurs when a model selects the correct tool but generates argument values in an inconsistent language, which we term Argument Language Mismatch (ALM). Although semantically correct, such outputs are operationally invalid and not captured by standard API-calling metrics. We revisit post-training strategies for mitigating ALM and find that, in our benchmark, supervised fine-tuning (SFT) provides a strong baseline, substantially improving argument language consistency and end-to-end function call accuracy. Under consistent model selection, SFT achieves performance comparable to, and sometimes exceeding more complex reinforcement learning (RL) approaches. We further examine whether RL with structured, argument-aware rewards offers additional benefits. While methods such as Group Relative Policy Optimization (GRPO) can improve language consistency and better preserve general reasoning ability, these gains are incremental and most pronounced in generalization and multi-objective trade-offs. Overall, our results suggest that much of the performance in multilingual API grounding can be achieved through careful supervised training, with RL providing targeted rather than fundamental improvements.

cs.CL↗

arXiv2020

Pyramidal Edge-maps and Attention based Guided Thermal Super-resolution

Guided super-resolution (GSR) of thermal images using visible range images is challenging because of the difference in the spectral-range between the images. This in turn means that there is significant texture-mismatch between the images, which manifests as blur and ghosting artifacts in the super-resolved thermal image. To tackle this, we propose a novel algorithm for GSR based on pyramidal edge-maps extracted from the visible image. Our proposed network has two sub-networks. The first sub-network super-resolves the low-resolution thermal image while the second obtains edge-maps from the visible image at a growing perceptual scale and integrates them into the super-resolution sub-network with the help of attention-based fusion. Extraction and integration of multi-level edges allows the super-resolution network to process texture-to-object level information progressively, enabling more straightforward identification of overlapping edges between the input images. Extensive experiments show that our model outperforms the state-of-the-art GSR methods, both quantitatively and qualitatively.

cs.CV↗

arXiv2019

Unsupervised Single Image Underwater Depth Estimation

Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken `in the wild' by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.

cs.CV↗

arXiv2018

Phase retrieval for Fourier Ptychography under varying amount of measurements

Fourier Ptychography is a recently proposed imaging technique that yields high-resolution images by computationally transcending the diffraction blur of an optical system. At the crux of this method is the phase retrieval algorithm, which is used for computationally stitching together low-resolution images taken under varying illumination angles of a coherent light source. However, the traditional iterative phase retrieval technique relies heavily on the initialization and also need a good amount of overlap in the Fourier domain for the successively captured low-resolution images, thus increasing the acquisition time and data. We show that an auto-encoder based architecture can be adaptively trained for phase retrieval under both low overlap, where traditional techniques completely fail, and at higher levels of overlap. For the low overlap case we show that a supervised deep learning technique using an autoencoder generator is a good choice for solving the Fourier ptychography problem. And for the high overlap case, we show that optimizing the generator for reducing the forward model error is an appropriate choice. Using simulations for the challenging case of uncorrelated phase and amplitude, we show that our method outperforms many of the previously proposed Fourier ptychography phase retrieval techniques.

cs.CV↗