Simultaneous HDR image reconstruction and denoising for dynamic scenes

Abstract

High dynamic range (HDR) images are usually generated by combining multiple photographs acquired with different exposure times. This approach, while effective, suffers from various drawbacks. The irradiance estimation is performed by combining, for each pixel, different exposure values at the same spatial position. This estimation scheme does not take advantage of the redundancy present in most images. Moreover, images must be perfectly aligned and objects must be in the exact same position in all frames in order to combine the different exposures. In this work, we propose a new HDR image generation approach that simultaneously copes with these problems and exploits image redundancy to produce a denoised result. A reference image is chosen and a patch-based approach is used to find similar pixels that are then combined for the irradiance estimation. This patch-based approach permits to obtain a denoised result and is robust to image misalignments and object motions. Results show significant improvements in terms of noise reduction over previous HDR image generation techniques, while being robust to motion and changes between the exposures.

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

“…Another advantage is that the saturated pixels are not organized in large regions. Indeed, some recent multi-image methods tackle motion problems by taking a reference image and then by estimating motion or reconstructing the image relative to this reference [31], [35]. A problem encountered by these approaches is the need to inpaint very large saturated and underexposed regions in the reference frame.…”

“…In the case of a static scene and a static camera, the combination of multiple images with different exposure levels is a simple and efficient solution [27], [29], [30]. However, several problems arise when either the camera or the elements in the scene move [31], [32].…”

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

“…In the usual experimental setting (usual temperature and exposure time equal to a fraction of a second) the dark current is negligible relative to the photo-electron count [2,23]. In the remainder of this paper, we set d = 0.…”

“…Within the model of (1.1), the expectation and the variance of any Poisson variable being equal, it is possible to compute [7,16,18,21,43] (1.2) E(u(x, y)) = gp(x, y) + μ, Var(u(x, y)) = g 2 p(x, y) + σ 2 ,…”

“…The raw output of a linear camera (i.e., the raw sensor output, without any image processing) is a function of the quantity of light photons arriving at a CCD sensor cell, but is affected by shot noise, a dark current, and readout noise. The following stochastic model (or slight variations) is often used [2,7,18,21,23], yielding a heteroscedastic signal-dependent noise:…”