Rohan Lal - Academia.edu (original) (raw)

Uploads

Papers by Rohan Lal

2019 International Conference on 3D Immersion (IC3D), 2019

In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3... more In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3D display. We are interested in the case where different exposures are taken from different viewpoints. The scheme employed a new depth-adaptive cross-trilateral filter (DA-CTF) for recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images captured at different exposure levels. Explicitly leveraging additional depth information in the tone mapping operation correctly identify global contrast change and detail visibility change by preserving the edges and reducing halo artifacts in the synthesized 3D views by depth-image-based rendering (DIBR) procedure. The experiments show that the proposed DA-CTF and DIBR scheme outperforms state-of-the-art operators in the enhanced depiction of tone mapped HDR stereo images on LDR displays.

European Light Field Imaging Workshop (ELFI 2019), Borovets, Bulgaria, In European Signal Processing Conference (EUSIPCO) Proceedings, 2019

Existing light field cameras are susceptible to produce low-quality results as sensor noise can d... more Existing light field cameras are susceptible to produce low-quality
results as sensor noise can dominate measurements. Thus, denoising
is a critical step in the light field (LF) subsequent analysis and
processing. This paper presents a novel LF denoising framework
based on an adaptive parallax and auto-regressive model analysis.
The novel procedure first creates a view-dependent LF stack
by compensating parallax variation employing an extended variational
flow technique on a set of LF intensity and depth features.
Further, it takes advantage of the spatial similarity across the registered
LF stack and reduce the noisy observations. The output is,
further, improved by formulating the denoising as a novel adaptive
autoregressive (AR) stochastic problem. The proposed convex
AR model averaged view-specific spatial energies of stacked LF
images on Riemannian manifolds by a depth-directed maximum
likelihood AR parameter estimation process. Lastly, scale intensity
of refined AR LF predicted view by the average intensity of
the superpixel in each LF stacked image. The experiments show
that proposed AR LF denoiser outperforms standard algorithms in
terms of visual quality and in preservation of parallax details.

9th International Conference on 3D Immersion (IC3D 2019), Brussels, Belgium, 2019

The consumer market of High Dynamic Range (HDR) displays and cameras is blooming rapidly with the... more The consumer market of High Dynamic Range (HDR) displays
and cameras is blooming rapidly with the advent of
3D video and display technologies. Specialised agencies like
Moving Picture Experts Group and International Telecommunication
Union are demanding the standardization of latest
display advancements. Lack of sufficient experimental
data is a major bottleneck for the development of preliminary
research efforts in 3D HDR video technology. We propose
to make publicly available to the research community,
a diversified database of Stereoscopic 3D HDR images and
videos, captured within the beautiful campus of Indian Institute
of Technology, Madras, which is blessed with rich flora
and fauna, and is home to several rare wildlife species. Further,
we have described the procedure of capturing, aligning,
calibrating and post-processing of 3D images and videos. We
have discussed research opportunities and challenges, and the
potential use cases of HDR stereo 3D applications and depthfrom-
HDR aspects.

Conference Presentations by Rohan Lal

European Light Field Imaging Workshop, Borovets, Bulgaria (European Association for Signal Processing, EURASIP), 2019

Existing light-field cameras are susceptible to produce low-quality results as sensor noise can d... more Existing light-field cameras are susceptible to produce low-quality results as sensor noise can dominate measurements. Thus, denoising is a critical step in the light-field (LF) subsequent analysis and processing. This paper presents a novel LF denoising framework based on an adaptive parallax and auto-regressive model analysis. The novel procedure first creates a view-dependent LF stack by compensating parallax variation employing an extended variational flow technique on a set of LF intensity and depth features. Further, it takes advantage of the spatial similarity across the registered LFs stack and reduce the noisy observations. The output is, further, improved by formulating the denoising as a novel adaptive autoregressive (AR) stochastic problem. The proposed convex AR model averaged view-specific spatial energies of stacked LF images on Riemannian manifolds by a depth-directed maximum likelihood AR parameter estimation process. Lastly, scale intensity of refined AR LF predicted view by the average intensity of the superpixel in each LF stacked image. The experiments show that proposed AR LF denoiser outperforms standard algorithms in terms of visual quality and in the preservation of parallax details.

2019 International Conference on 3D Immersion (IC3D), 2019

In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3... more In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3D display. We are interested in the case where different exposures are taken from different viewpoints. The scheme employed a new depth-adaptive cross-trilateral filter (DA-CTF) for recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images captured at different exposure levels. Explicitly leveraging additional depth information in the tone mapping operation correctly identify global contrast change and detail visibility change by preserving the edges and reducing halo artifacts in the synthesized 3D views by depth-image-based rendering (DIBR) procedure. The experiments show that the proposed DA-CTF and DIBR scheme outperforms state-of-the-art operators in the enhanced depiction of tone mapped HDR stereo images on LDR displays.

European Light Field Imaging Workshop (ELFI 2019), Borovets, Bulgaria, In European Signal Processing Conference (EUSIPCO) Proceedings, 2019

Existing light field cameras are susceptible to produce low-quality results as sensor noise can d... more Existing light field cameras are susceptible to produce low-quality
results as sensor noise can dominate measurements. Thus, denoising
is a critical step in the light field (LF) subsequent analysis and
processing. This paper presents a novel LF denoising framework
based on an adaptive parallax and auto-regressive model analysis.
The novel procedure first creates a view-dependent LF stack
by compensating parallax variation employing an extended variational
flow technique on a set of LF intensity and depth features.
Further, it takes advantage of the spatial similarity across the registered
LF stack and reduce the noisy observations. The output is,
further, improved by formulating the denoising as a novel adaptive
autoregressive (AR) stochastic problem. The proposed convex
AR model averaged view-specific spatial energies of stacked LF
images on Riemannian manifolds by a depth-directed maximum
likelihood AR parameter estimation process. Lastly, scale intensity
of refined AR LF predicted view by the average intensity of
the superpixel in each LF stacked image. The experiments show
that proposed AR LF denoiser outperforms standard algorithms in
terms of visual quality and in preservation of parallax details.

9th International Conference on 3D Immersion (IC3D 2019), Brussels, Belgium, 2019

The consumer market of High Dynamic Range (HDR) displays and cameras is blooming rapidly with the... more The consumer market of High Dynamic Range (HDR) displays
and cameras is blooming rapidly with the advent of
3D video and display technologies. Specialised agencies like
Moving Picture Experts Group and International Telecommunication
Union are demanding the standardization of latest
display advancements. Lack of sufficient experimental
data is a major bottleneck for the development of preliminary
research efforts in 3D HDR video technology. We propose
to make publicly available to the research community,
a diversified database of Stereoscopic 3D HDR images and
videos, captured within the beautiful campus of Indian Institute
of Technology, Madras, which is blessed with rich flora
and fauna, and is home to several rare wildlife species. Further,
we have described the procedure of capturing, aligning,
calibrating and post-processing of 3D images and videos. We
have discussed research opportunities and challenges, and the
potential use cases of HDR stereo 3D applications and depthfrom-
HDR aspects.

European Light Field Imaging Workshop, Borovets, Bulgaria (European Association for Signal Processing, EURASIP), 2019

Existing light-field cameras are susceptible to produce low-quality results as sensor noise can d... more Existing light-field cameras are susceptible to produce low-quality results as sensor noise can dominate measurements. Thus, denoising is a critical step in the light-field (LF) subsequent analysis and processing. This paper presents a novel LF denoising framework based on an adaptive parallax and auto-regressive model analysis. The novel procedure first creates a view-dependent LF stack by compensating parallax variation employing an extended variational flow technique on a set of LF intensity and depth features. Further, it takes advantage of the spatial similarity across the registered LFs stack and reduce the noisy observations. The output is, further, improved by formulating the denoising as a novel adaptive autoregressive (AR) stochastic problem. The proposed convex AR model averaged view-specific spatial energies of stacked LF images on Riemannian manifolds by a depth-directed maximum likelihood AR parameter estimation process. Lastly, scale intensity of refined AR LF predicted view by the average intensity of the superpixel in each LF stacked image. The experiments show that proposed AR LF denoiser outperforms standard algorithms in terms of visual quality and in the preservation of parallax details.