General Information
What I am good at
Research Projects
Deep learning based image super-resolution for low-resolution image recognition

Created deep learning based gradient image super-resolution network with faster convergence and memory optimization to preserve SIFT features for recognition of low-resolution images; Super-resolved Difference of Gaussian (DoG) images are integrated to SIFT which exhibits around 10-12 SIFT matching points gain over the state of the art super-resolution method EDSR.

Deep Neural Network based Video Frame Interpolation

Using Deep Learning, middle frame from a video is predicted from the previous and following frames and the result exhibits a 3dB gain over bi-cubic interpolation.

Genomic Data Compression for reference free sequence

DNA sequence being mapped into codon level and then converted into character text data are modelled and mapped to form a text file for the learning based compression, e.g., PAQ which is the combination of neural network text prediction and arithmetic coding.

Rate Agnostic Video Content Identification and De-duplication using Machine Learning

Using GPAC tools, same video with different resolution is converted to different bit rates. For each one, the video is divided into different segments,each denoting a video content. From each segment, image frames are extracted using FFMPEG. For memory optimization, only the top features with larger eigenvalues from SIFT are taken applying a dimensional reduction by PCA and then the Fisher Aggregation is done for different GMM models.

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