DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Testing two-phase transition signaling based self-timed circuits in a synthesis environment

Journal ArticleThe problem of testing self-timed circuits generated by an automatic synthesis system is studied. Two-phase transition signalling is assumed and the circuits are targetted for an asynchronous macromodule based implementation as in [?, ?, ?, ?]. The partitioning of the circuits into control blocks, function blocks, and predicate (conditional) blocks, originally conceived for synthesis purpose, is found to be very elegant and appropriate for test generation. The problem of data dependent control flow is solved by introducing a new macromodule called SCANSELECT (SELECT with scan). Algorithms for test generation are based on the Petri-net like representation of the physical circuit. The techniques are illustrated on the high-level synthesis system called SHILPA being developed by the Author's

CapsFlow: Optical Flow Estimation with Capsule Networks

We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks. Most of the existing state of the art deep architectures either uses a correlation oepration to match features from them. While correlation layer is sensitive to the choice of hyperparameters and does not put a prior on the underlying structure of the object, spatio temporal features will be limited by the network's receptive field. Also, we as humans look at moving objects as whole, something which cannot be encoded by correlation or spatio temporal features. Capsules, on the other hand, are specialized to model seperate entities and their pose as a continuous matrix. Thus, we show that a simpler linear operation over poses of the objects detected by the capsules in enough to model flow. We show reslts on a small toy dataset where we outperform FlowNetC and PWC-Net models.Comment: Newer version added to correct issue in the conference name of the previous version uploaded on April 1s

Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images.Comment: BMVC 2021 Best Student Paper Award (Runner-Up). Project Page: https://val.cds.iisc.ac.in/HDR/BMVC21/index.htm

Biofabrication of Silver Oxide Nanoparticles (SO-NP) by autolysate of Pseudomonas mendocina PM1, and assessment of its antimicrobial/antibiofilm potential

Silver oxide Nanoparticles (SO-NP) exhibit excellent light absorbing, semi conducting properties and hence are employed in wide range of applications such as catalyst, biosensors, and in fuel cells. Green synthesis of nanoparticles using different microorganisms is a widely accepted since this method is cheap and eco-friendly. Nanoparticles synthesized by this route are smaller in size, highly stable, show high reactivity and stability. In this context, biofabrication of Silver Oxide Nanoparticles (SO-NP) by autolysate of Pseudomonas mendocina PM1 has been evaluated. Synthesis of SO-NP was observed, when autolysate of P. mendocina PM1 was incubated with 0.5 mM AgNO3 in dark for 24 h. Synthesis of SO-NP was confirmed by UV-Vis analysis. SO-NP was further confirmed by Transmission Electron Microscopy (TEM) and X-ray Diffraction (XRD) which confirmed presence of SO-NP. XRD revealed that SO-NP where of the type Ag3O4. FTIR analysis indicated that peptides were involved in the reduction and stability of SO-NP. SO-NP’s showed potent anti-microbial/ anti-biofilm activity against common pathogenic/non-pathogenic bacteria. This is the first report of synthesis of SO-NP by P. mendocina PM

Conservation of Human Microsatellites across 450 Million Years of Evolution

The sequencing and comparison of vertebrate genomes have enabled the identification of widely conserved genomic elements. Chief among these are genes and cis-regulatory regions, which are often under selective constraints that promote their retention in related organisms. The conservation of elements that either lack function or whose functions are yet to be ascribed has been relatively little investigated. In particular, microsatellites, a class of highly polymorphic repetitive sequences considered by most to be neutrally evolving junk DNA that is too labile to be maintained in distant species, have not been comprehensively studied in a comparative genomic framework. Here, we used the UCSC alignment of the human genome against those of 11 mammalian and five nonmammalian vertebrates to identify and examine the extent of conservation of human microsatellites in vertebrate genomes. Out of 696,016 microsatellites found in human sequences, 85.39% were conserved in at least one other species, whereas 28.65% and 5.98% were found in at least one and three nonprimate species, respectively. An exponential decline of microsatellite conservation with increasing evolutionary time, a comparable distribution of conserved versus nonconserved microsatellites in the human genome, and a positive correlation between microsatellite conservation and overall sequence conservation, all suggest that most microsatellites are only maintained in genomes by chance, although exceptionally conserved human microsatellites were also found in distant mammals and other vertebrates. Our findings provide the first comprehensive survey of microsatellite conservation across deep evolutionary timescales, in this case 450 Myr of vertebrate evolution, and provide new tools for the identification of functional conserved microsatellites, the development of cross-species microsatellite markers and the study of microsatellite evolution above the species level

De Novo Genesis of Enhancers in Vertebrates

Whole genome duplication in teleost fish reveals that a few changes in non-regulatory genomic sequences are a source for generating new enhancers