Indian Institute of Technology Ropar

About: Indian Institute of Technology Ropar is a education organization based out in Ropar, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1014 authors who have published 2878 publications receiving 35715 citations.

NTIRE 2021 NonHomogeneous Dehazing Challenge Report

Abstract: This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous Dehazing. The proposed techniques and their results have been evaluated on a novel dataset that extends the NH-Haze datset. It consists of additional 35 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. The nonhomogeneous haze has been introduced in the outdoor scenes by using a a professional setup that imitates the real conditions of haze scenes. 327 participants registered in the challenge and 23 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing.

Wettability of Complex Fluids and Surfactant Capped Nanoparticle-Induced Quasi-Universal Wetting Behavior.

Abstract: Even though there are quite large studies on wettability of aqueous surfactants and a few studies on effects of nanoparticles on wettability of colloids, to the best of authors’ knowledge, there is no study reported on the combined effect of surfactant and nanoparticles in altering the wettability. The present study, for the first time, reports an extensive experimental and theoretical study on the combined effect of surfactants and nanoparticles on the wettability of complex fluids such as nanocolloids on different substrates, ranging from hydrophilic with a predominantly polar surface energy component (silicon wafer and glass) to near hydrophobic range with a predominantly dispersive component of surface energy (aluminum and copper substrates). Systematically planned experiments are carried out to segregate the contributing effects of surfactants, particles, and combined particle and surfactants in modulating the wettability. The mechanisms and the governing parameters behind the interactions of nanocol...

Colorimetric and fluorescent “on–off” chemosensor for Cu2+ in semi-aqueous medium

Abstract: A colorimetric and fluorescent ‘on–off’ chemosensor 1 was developed for the selective and sensitive detection of Cu 2+ over the other examined metal ions. Upon addition of Cu 2+ , sensor 1 exhibits moderate enhancement in the absorbance intensity with a naked-eye detectable colour change from colourless to yellow in DMSO/H 2 O (50:50, v/v). Also, the emission of 1 was selectively quenched in the presence Cu 2+ with fast response and the detection limit was found to be 50 nM.

LEARNet Dynamic Imaging Network for Micro Expression Recognition.

Abstract: Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the dynamic representation of micro-expressions to preserve facial movement information of a video in a single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region. The LEARNet refines the salient expression features in accretive manner by incorporating accretion layers (AL) in the network. The response of the AL holds the hybrid feature maps generated by prior laterally connected convolution layers. Moreover, LEARNet architecture incorporates the cross decoupled relationship between convolution layers which helps in preserving the tiny but influential facial muscle change information. The visual responses of the proposed LEARNet depict the effectiveness of the system by preserving both high- and micro-level edge features of facial expression. The effectiveness of the proposed LEARNet is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC. The experimental results after investigation show a significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II, CAS(ME)^2 and SMIC datasets respectively.