Search Results for author: A. N. Rajagopalan

Found 35 papers, 7 papers with code

Unfolding a blurred image

no code implementations28 Jan 2022 Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan

This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.

Deblurring Image Deblurring +1

Deep Networks for Image and Video Super-Resolution

no code implementations28 Jan 2022 Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan

To enable super-resolution for multiple factors, we propose a scale-recurrent framework which reutilizes the filters learnt for lower scale factors recursively for higher factors.

Image Super-Resolution Single Image Super Resolution +1

Image Superresolution using Scale-Recurrent Dense Network

no code implementations28 Jan 2022 Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan

In this paper, we propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) that allow extraction of abundant local features from the image.

Image Super-Resolution

Adaptive Image Inpainting

no code implementations1 Jan 2022 Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

We deploy cross and self distillation techniques and discuss the need for a dedicated completion-block in encoder to achieve the distillation target.

Image Inpainting

Mitigating Channel-wise Noise for Single Image Super Resolution

no code implementations14 Dec 2021 Srimanta Mandal, Kuldeep Purohit, A. N. Rajagopalan

In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches.

Image Super-Resolution Single Image Super Resolution

Deep network for rolling shutter rectification

no code implementations12 Dec 2021 Praveen K, Lokesh Kumar T, A. N. Rajagopalan

The motion block predicts camera pose for every row of the input RS distorted image while the trajectory module fits estimated motion parameters to a third-order polynomial.

IR Motion Deblurring

no code implementations23 Nov 2021 Nisha Varghese, Mahesh Mohan M. R., A. N. Rajagopalan

Such datasets which are a rarity can be a valuable asset for contemporary deep learning methods.

Deblurring

Spatially-Adaptive Image Restoration using Distortion-Guided Networks

no code implementations ICCV 2021 Kuldeep Purohit, Maitreya Suin, A. N. Rajagopalan, Vishnu Naresh Boddeti

However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image.

Image Restoration

Gated Spatio-Temporal Attention-Guided Video Deblurring

no code implementations CVPR 2021 Maitreya Suin, A. N. Rajagopalan

Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur.

Deblurring

Localize to Binauralize: Audio Spatialization From Visual Sound Source Localization

1 code implementation ICCV 2021 Kranthi Kumar Rachavarapu, Aakanksha, Vignesh Sundaresha, A. N. Rajagopalan

Through user study, we further validate that our proposed approach generates binaural-quality audio using as little as 10% of explicit binaural supervision data for the SG network.

Audio Generation

Distillation-Guided Image Inpainting

no code implementations ICCV 2021 Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

Image inpainting methods have shown significant improvements by using deep neural networks recently.

Image Inpainting

Unpaired Image Denoising

1 code implementation24 Sep 2020 Priyatham Kattakinda, A. N. Rajagopalan

A majority of methods for image denoising are no exception to this rule and hence demand pairs of noisy and corresponding clean images.

Image and Video Processing

Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring

no code implementations CVPR 2020 Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comes at the expense of of the increase in model size and inference speed.

Ranked #14 on Image Deblurring on GoPro (using extra training data)

Deblurring Image Deblurring

Planar Geometry and Image Recovery from Motion-Blur

no code implementations7 Apr 2019 Kuldeep Purohit, Subeesh Vasu, M. Purnachandra Rao, A. N. Rajagopalan

We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation.

Deblurring

Motion Deblurring with an Adaptive Network

no code implementations25 Mar 2019 Kuldeep Purohit, A. N. Rajagopalan

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur.

Ranked #13 on Image Deblurring on GoPro (using extra training data)

Deblurring Image Deblurring

Unsupervised Class-Specific Deblurring

no code implementations ECCV 2018 Thekke Madam Nimisha, Kumar Sunil, A. N. Rajagopalan

To improve the stability of GAN and to preserve the image correspondence, we introduce an additional CNN module that reblurs the generated GAN output to match with the blurred input.

Deblurring

Occlusion-Aware Rolling Shutter Rectification of 3D Scenes

no code implementations CVPR 2018 Subeesh Vasu, Mahesh Mohan M. R., A. N. Rajagopalan

Due to the sequential mechanism, images acquired with a moving camera are subjected to rolling shutter effect which manifests as geometric distortions.

Divide and Conquer for Full-Resolution Light Field Deblurring

no code implementations CVPR 2018 M. R. Mahesh Mohan, A. N. Rajagopalan

Consequently, blind deblurring of any single subaperture image elegantly paves the way for cost-effective non-blind deblurring of the other subaperture images.

Deblurring

Bringing Alive Blurred Moments

1 code implementation CVPR 2019 Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan

This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.

Ranked #22 on Image Deblurring on GoPro (using extra training data)

Deblurring Image Deblurring +1

From Local to Global: Edge Profiles to Camera Motion in Blurred Images

no code implementations CVPR 2017 Subeesh Vasu, A. N. Rajagopalan

In this work, we investigate the relation between the edge profiles present in a motion blurred image and the underlying camera motion responsible for causing the motion blur.

Deblurring Motion Estimation

Unrolling the Shutter: CNN to Correct Motion Distortions

no code implementations CVPR 2017 Vijay Rengarajan, Yogesh Balaji, A. N. Rajagopalan

Our single-image correction method fares well even operating in a frame-by-frame manner against video-based methods and performs better than scene-specific correction schemes even under challenging situations.

Rolling Shutter Super-Resolution

no code implementations ICCV 2015 Abhijith Punnappurath, Vijay Rengarajan, A. N. Rajagopalan

But CMOS sensors that have increasingly started to replace their more expensive CCD counterparts in many applications do not respect this assumption if there is a motion of the camera relative to the scene during the exposure duration of an image because of the row-wise acquisition mechanism.

Image Super-Resolution

Cannot find the paper you are looking for? You can Submit a new open access paper.