Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light has seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark dataset such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing visualizations of both hand-crafted and learned features. Most importantly, we found that the effects of low-light reaches far deeper into the features than can be solved by simple "illumination invariance'". It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The Exclusively Dark dataset with its annotation is available at this https URL

Getting to Know Low-light Images with The Exclusively Dark Dataset

Surface and Ground Water, Weathering, and Soils

This paper provides an overview of the Curiosity rover's exploration at Vera Rubin ridge and summarizes the science results. Vera Rubin ridge (VRR) is a distinct geomorphic feature on lower Aeolis Mons (informally known as Mt. Sharp) that was identified in orbital data based on its distinct texture, topographic expression, and association with a hematite spectral signature. Curiosity conducted extensive remote sensing observations, acquired data on dozens of contact science targets, and drilled three outcrop samples from the ridge, as well as one outcrop sample immediately below the ridge. Our observations indicate that strata composing VRR were deposited in a predominantly lacustrine setting and are part of the Murray formation. The rocks within the ridge are chemically in family with underlying Murray formation strata. Red hematite is dispersed throughout much of the VRR bedrock, and this is the source of the orbital spectral detection. Gray hematite is also present in isolated, gray‐colored patches concentrated towards the upper elevations of VRR, and these gray patches also contain small, dark Fe‐rich nodules. We propose that VRR formed when diagenetic event(s) preferentially hardened rocks, which were subsequently eroded into a ridge by wind. Diagenesis also led to enhanced crystallization and/or cementation that deepened the ferric‐related spectral absorptions on the ridge, which helped make them readily distinguishable from orbit. Results add to existing evidence of protracted aqueous environments at Gale crater and give new insight into how diagenesis shaped Mars’ rock record.

Evidence for a Diagenetic Origin of Vera Rubin Ridge, Gale Crater, Mars: Summary and Synthesis of Curiosity's Exploration Campaign

Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India

. Groundwater origin, flow and geochemical evolution in the Golmud
River watershed of the Qaidam Basin was assessed using hydrogeochemical,
isotopic and numerical approaches. The stable isotopic results show
groundwater in the basin originates from precipitation and meltwater in the
mountainous areas of the Tibetan Plateau. Modern water was found in the
alluvial fan and shallow aquifers of the loess plain. Deep confined
groundwater was recharged by paleowater during the late Pleistocene and
Holocene under a cold climate. Groundwater in the low-lying depression of the
central basin is composed of paleobrines migrated from the western part of
the basin due to tectonic uplift in the geological past. Groundwater
chemistry is controlled by mineral dissolution (halite, gypsum, anhydrite, mirabilite), silicate weathering, cation exchange, evaporation and mineral precipitation
(halite, gypsum, anhydrite, aragonite, calcite, dolomite) and
varies from fresh to brine with the water types evolving from
 HCO3 ⋅ Cl - Ca ⋅ Mg ⋅ Na to Cl - Na , Cl - K - Na 
and Cl - Mg type along the flow path. Groundwater flow patterns are closely
related to stratigraphic control and lithological distribution. Three
hierarchical groundwater flow systems, namely local, intermediate and
regional, were identified using numerical modeling. The quantity of water
discharge from these three systems accounts for approximately 83 %, 14 %
and 3 %, respectively, of the total groundwater quantity of the watershed.
This study can enhance the understanding of groundwater origin, circulation
and evolution in the Qaidam Basin as well as other arid endorheic watersheds
in northwestern China and elsewhere worldwide.

/pdf/groundwater-origin-flow-regime-and-geochemical-evolution-in-4wib6bndgk.pdf

Groundwater origin, flow regime and geochemical evolution in arid endorheic watersheds: a case study from the Qaidam Basin, northwestern China

Influence of geology on groundwater–sediment interactions in arsenic enriched tectono-morphic aquifers of the Himalayan Brahmaputra river basin

Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, the current iterative-optimization-based reconstruction algorithms produce noisier and perceptually poorer images. In this work, we propose a non-iterative deep learning-based reconstruction approach that results in orders of magnitude improvement in image quality for lensless reconstructions. Our approach, called FlatNet, lays down a framework for reconstructing high-quality photorealistic images from mask-based lensless cameras, where the camera's forward model formulation is known. FlatNet consists of two stages: (1) an inversion stage that maps the measurement into a space of intermediate reconstruction by learning parameters within the forward model formulation, and (2) a perceptual enhancement stage that improves the perceptual quality of this intermediate reconstruction. These stages are trained together in an end-to-end manner. We show high-quality reconstructions by performing extensive experiments on real and challenging scenes using two different types of lensless prototypes: one which uses a separable forward model and another, which uses a more general non-separable cropped-convolution model. Our end-to-end approach is fast, produces photorealistic reconstructions, and is easy to adopt for other mask-based lensless cameras.

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Chlorate as a Potential Oxidant on Mars: Rates and Products of Dissolved Fe(II) Oxidation

Light Field (LF) offers unique advantages such as post-capture refocusing and depth estimation, but low-light conditions limit these capabilities. To restore low-light LFs we should harness the geometric cues present in different LF views, which is not possible using single-frame low-light enhancement techniques. We, therefore, propose a deep neural network for Low-Light Light Field (L3F) restoration, which we refer to as L3Fnet. The proposed L3Fnet not only performs the necessary visual enhancement of each LF view but also preserves the epipolar geometry across views. We achieve this by adopting a two-stage architecture for L3Fnet. Stage-I looks at all the LF views to encode the LF geometry. This encoded information is then used in Stage-II to reconstruct each LF view. To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. For each scene, we captured four LFs, one with near-optimal exposure and ISO settings and the others at different levels of low-light conditions varying from low to extreme low-light settings. The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. To further analyze the performance of low-light reconstruction methods, we also propose an L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we propose a novel pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF.

Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging

Chlorate is an important Cl-bearing species and a strong potential Fe(II) oxidant on Mars. Since the amount of oxychlorine species (perchlorate and chlorate) detected on Mars is limited (<~1 wt.%), the effectiveness of chlorate to produce iron oxides depends heavily on its oxidizing capacity. Decomposition of chlorate or intermediates produced during its reduction, before reaction with Fe(II) would decrease its effective capacity as an oxidant. We thus evaluated the capacity of chlorate to produce Fe(III) minerals in Mars-relevant fluids, via oxidation of dissolved Fe(II). Each chlorate ion can oxidize 6 Fe(II) ions under all conditions investigated. Mass balance demonstrated that 1 wt.% chlorate (as ClO3−) could produce approximately 6 to 12 wt.% Fe(III) or mixed valent mineral products, with the amount varying with the formula of the precipitating phase. The mineral products are primarily determined by the fluid type (chloride- or sulfate-rich), the solution pH, and the rate of Fe(II) oxidation. The pH at the time of initial mineral nucleation and the amount of residual dissolved Fe(II) in the system exert important additional controls on the final mineralogy. Subsequent diagenetic transformation of these phases would yield 5.7 wt.% hematite per wt.% of chlorate reacted, providing a quantitative constraint on the capacity of chlorate to generate iron oxides on Mars.

https://www.mdpi.com/2075-163X/10/9/729/pdf

Kaushik Mitra

Papers

Influence of geology on groundwater–sediment interactions in arsenic enriched tectono-morphic aquifers of the Himalayan Brahmaputra river basin

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Chlorate as a Potential Oxidant on Mars: Rates and Products of Dissolved Fe(II) Oxidation

Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging

Capacity of Chlorate to Oxidize Ferrous Iron: Implications for Iron Oxide Formation on Mars