Manoj Kumar Sharma

Selective edge enhancement using anisotropic vortex filter

In optical image processing, selective edge enhancement is important when it is preferable to emphasize some edges of an object more than others. We propose a new method for selective edge enhancement of amplitude objects using the anisotropic vortex phase mask by introducing anisotropy in a conventional vortex mask with the help of the sine function. The anisotropy is capable of edge enhancement in the selective region and in the required direction by changing the power and offset angle, respectively, of the sine function.

A miniaturized nanobiosensor for choline analysis

A novel reusable chemiluminescence choline nanobiosensor has been developed using aligned zinc oxide nanorod-films (ZnONR). The chemically fashioned ZnONR were synthesized by hybrid wet chemical route onto glass substrates and used to fabricate a stable chemiluminescent choline biosensor. The biosensor was constructed by co-immobilization of the enzymes choline oxidase and peroxidase. The covalent immobilization of the enzymes on the ZnONR was achieved using 16-phosphonohexadecanoic acid as a cross-linker. The phosphonation of the ZnONR imparted significant stability to the immobilized enzyme as against physisorbed enzyme. A lower value of Michaelis-Menten constant (Km), of 0.062 mM for the covalently coupled enzyme over the physisorbed enzymes facilitated enhanced stability of ZnONR nanobiosensor. The ZnONR-choline biosensor has been investigated over a wide range of choline from 0.0005 mM to 2 mM. Importantly, the recovery of choline in milk samples was close to 99%. Using the developed biosensor, choline was measurable even after 30 days with 60 repeated measurements proving the stability of the sensor (Intraday RSD%=2.83 and Interday RSD%=3.51).

Generation of spatial coherence comb using Dammann grating

A new technique to generate a spatially varying coherence field, such as a coherence comb using a Dammann grating, is proposed and experimentally demonstrated. The principle of the technique lies with the vectorial van Cittert-Zernike theorem, which connects vectorial source structure with the coherence-polarization of the light. The Dammann grating is encoded into one of the polarization components of the light to shape the vectorial source structure and, consequently, the coherence-polarization of the light. Experimental results on the generation of a spatial coherence comb by the Dammann grating are presented for different orders.

Phase imaging using spiral-phase diversity

We demonstrate that the two-dimensional quadrature transform property of a spiral-phase filter may be utilized for addressing the noninterferometric iterative phase imaging problem. Two intensity measurements for an unknown input object are performed in the back focal (Fourier transform) plane of a lens with and without a spiral-phase mask in the lens aperture. It is shown that the two intensity measurements along with the aperture support constraint can be used for estimating the phase of an unknown input object with an iterative algorithm. Numerical simulations are presented for comparison of the new spiral-phase diversity technique and the more standard defocus-diversity method. Experimental results for the spiral-phase diversity are also shown to illustrate the effectiveness of this approach for imaging of amplitude/phase objects.

Analysis of Fibonacci gratings and their diffraction patterns

Aperiodic and fractal optical elements are proving to be promising candidates in image-forming devices. In this paper, we analyze the diffraction patterns of Fibonacci gratings (FbGs), which are prototypical examples of aperiodicity. They exhibit novel characteristics such as redundancy and robustness that keep their imaging characteristics intact even when there is significant loss of information. FbGs also contain fractal signatures and are characterized by a fractal dimension. Our study suggests that aperiodic gratings may be better than their fractal counterparts in technologies based on such architectures. We also identify the demarcating features of aperiodic and fractal diffraction, which have been rather fuzzy in the literature so far.

Robustness of Cantor Diffractals

Diffractals are electromagnetic waves diffracted by a fractal aperture. In an earlier paper, we reported an important property of Cantor diffractals, that of redundancy [R. Verma et. al., Opt. Express 20, 8250 (2012)]. In this paper, we report another important property, that of robustness. The question we address is: How much disorder in the Cantor grating can be accommodated by diffractals to continue to yield faithfully its fractal dimension and generator? This answer is of consequence in a number of physical problems involving fractal architecture.

Oxygen vacancy mediated large magnetization in chemically synthesized Ni-doped HfO2 nanoparticle powder samples

A cost-effective solution based chemical method of synthesizing nanostructured Hf1−xNixO2 with 0u2009≤u2009xu2009≤u20090.05 in powder form, from easily available laboratory reagents is presented. Production of uniformly shaped and sized (13–16 nm) nanoparticles with excellent crystallinity is demonstrated by transmission electron microscopy, x-ray diffraction (XRD) studies and Raman spectra. The origin of ferromagnetism in the Ni-doped HfO2 nanoparticle powder samples is investigated. Magnetization studies along with x-ray photoelectron spectroscopy (XPS) studies suggest that some of the Ni-ions are substitutionally incorporated in HfO2 host matrix. The XPS studies also show the presence of a small fraction of Ni metal (most likely Ni nanoclusters), undetected in standard XRD for lightly doped samples, suggesting that the observed room temperature ferromagnetism is at least partly due to Ni nanoclusters. The observed large value (∼6 emu/g) of magnetization, may not be entirely due to the presence of Ni metal cluster, can...

Deep learning based frameworks for image super-resolution and noise-resilient super-resolution

Our paper is motivated from the advancement in deep learning algorithms for various computer vision problems. We are proposing a novel end-to-end deep learning based framework for image super-resolution. This framework simultaneously calculates the convolutional features of low-resolution (LR) and high-resolution (HR) image patches and learns the non-linear function that maps these convolutional features of LR image patches to their corresponding HR image patches convolutional features. Here, proposed deep learning based image super-resolution architecture is termed as coupled deep convolutional auto-encoder (CDCA) which provides state-of-the-art results. Super-resolution of a noisy/distorted LR images results in noisy/distorted HR images, as super-resolution process gives rise to spatial correlation in the noise, and further, it cannot be de-noised successfully. Traditional noise resilient image super-resolution methods utilize a de-noising algorithm prior to super-resolution but de-noising process gives rise to loss of some high-frequency information (edges and texture details) and super-resolution of the resultant image provides HR image with missing edges and texture information. We are also proposing a novel end-to-end deep learning based framework to obtain noise resilient image super-resolution. Proposed end-to-end deep learning based framework for noise resilient super-resolution simultaneously perform image de-noising and super-resolution as well as preserves textural details. First, stacked sparse de-noising auto-encoder (SSDA) was learned for LR image de-noising and proposed CDCA was learned for image superresolution. Then, both image de-noising and super-resolution networks were cascaded. This cascaded deep learning network was employed as one integral network where pre-trained weights were serving as initial weights. The integral network was end-to-end trained or fine-tuned on a database having noisy, LR image as an input and target as an HR image. In fine-tuning, all layers of the combined end-to-end network was jointly optimized to perform image de-noising and super-resolution simultaneously. Experimental results show that proposed noise resilient super-resolution framework outperforms the conventional and state-of-the-art approaches in terms of PSNR and SSIM metrics.

Composite vortex filters

Superposition of two optical vortex beams form composite vortex beam and the characteristics of the resultant depend on the separation and the relative phases of the interfering vortex beams. We analyze these composite vortex filters.

Space-Time Super-Resolution Using Deep Learning Based Framework

This paper introduces a novel end-to-end deep learning framework to learn space-time super-resolution (SR) process. We propose a coupled deep convolutional auto-encoder (CDCA) which learns the non-linear mapping between convolutional features of up-sampled low-resolution (LR) video sequence patches and convolutional features of high-resolution (HR) video sequence patches. The upsampling in LR video refers to tri-cubic interpolation both in space and time. We also propose a H.264/AVC compatible video space-time SR framework by using learned CDCA, which enables to super-resolve compressed LR video with less computational complexity. The experimental results prove that the proposed H.264/AVC compatible framework performs better than the state-of-art techniques on space-time SR in terms of quality and time complexity.