Title

DEEP JOINT DEINTERLACING AND DENOISING FOR SINGLE SHOT DUAL-ISO HDR RECONSTRUCTION

Abstract

Abstract

HDR (High Dynamic Range) images have traditionally been obtained by merging multiple

exposures each captured with a different exposure time. However, this

approach entails longer capture times and necessitates deghosting if

the captured scene contains moving objects. With the advent of modern

camera sensors that can perform per-pixel exposure modulation, it is

now possible to capture all of the required exposures within a single

shot. The new challenge then becomes how to best combine different

pixels with different exposure values into a single full-resolution

and low-noise HDR image. In this thesis, we propose a joint

multi-exposure frame deinterlacing and denoising algorithm powered by

deep convolutional neural networks (DCNN). In our algorithm, we first

train two DCNNs, with one tuned for reconstructing low exposures and

the other for high exposures. Each DCNN takes the same mosaicked

dual-ISO input image and outputs either the low exposure or high

exposure depending on the type of the network. The resulting

exposures can be demosaicked and converted to the desired target color

space prior to HDR assembly. Our evaluations using computational

metrics as well as visual comparisons indicate that the quality of our

reconstructions significantly surpasses the state-of-the-art in this

field.

Biography:

Supervisor(s)

Supervisor(s)

UGUR COGALAN

Date and Location

Date and Location

2019-04-04;12:15:00-A105

Category

Category

MSc_Thesis