Abstract
The quality of a high dynamic range (HDR) image produced from bracketed images taken at different camera exposure times can be degraded by noise contained in bracketed images. In this paper, we propose a noise reduction method on bracketed images based on exposure time ratio. First, for each pixel pair of a same scene point lying on two different images, the ratio of their intensity values is compared with the ratio of exposure times of the images on which the pixels are lying. If the compared ratios are close, these two pixels are included in noise-free pixels set. The complement of noise-free pixels set is defined as noisy pixels set. Then, the intensity value of each pixel in noisy pixels set is corrected by its expected value computed from noise-free pixel of the same scene point lying on another image. Experimental results show that all the noisy intensity values can be correctly restored from noise-free pixels except the saturated pixels. Denoising results by PSNR show that the proposed method outperforms the other recent denoising methods such as based-on pixel density filter (BPDF), noise adaptive fuzzy switching median filter (NAFSMF), and adaptive Riesz mean filter (ARmF).
Highlights
The human visual system (HVS) can perceive higher dynamic range than most of the cameras
A noise reduction method based on exposure time ratio on three bracketed images has been proposed
For each location (i, j), the ratio of intensity values of the corresponding pixels between two images is equivalent to gamma-corrected exposure time ratio which can be computed from a priori knowledge of camera parameters
Summary
The human visual system (HVS) can perceive higher dynamic range (i.e., the ratio between the maximum and minimum intensity values in an image) than most of the cameras. The images acquired in bracketing process suffer from noise that usually occurs during acquisition This noise can degrade the quality of the final constructed HDR image. If the ratio of the two corresponding pixels’ intensity values is close to the exposure time ratio, these two pixels can be assumed to be noise-free. In this way, all the pixels in the bracketed images are checked whether they are noise-free, and the classified noise-free pixels are grouped into the noise-free pixels set. Experimental results on several datasets illustrate that the proposed method can correctly restore intensity value of any noisy pixel when there exists corresponding noisy-free pixel of the same scene point in another image.
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