Abstract
Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.
Highlights
With the rapid development of complementary metal oxide semiconductor (CMOS)technologies, CMOS image sensors have become popular with consumer and vehicle electronics, telemedicine, video surveillance, space exploration, fluorescence detection, and so on [1,2,3,4]
The other is based on calculation from single image. The latter is broadly classified into two categories viz. methods based on variance stabilization transformation (VST) and methods based on fitting sample pairs
To improve the overall accuracy of selecting these weakly textured image blocks and estimating the noise parameters of a noisy image thereafter, we propose a novel methodology based on local binary cyclic jumping as applied to a Poisson–Gaussian signaldependent noise model
Summary
With the rapid development of complementary metal oxide semiconductor (CMOS)technologies, CMOS image sensors have become popular with consumer and vehicle electronics, telemedicine, video surveillance, space exploration, fluorescence detection, and so on [1,2,3,4]. For CMOS image sensors, a signaldependent noise model, such as the Poisson–Gaussian model, can more accurately delineate the noise characteristics than an additive channel-dependent noise model [10,11,12,13,14,15,16,17,18,19]. Past studies have engineered various noise parameter estimations methods to adopt the Poisson–Gaussian signal-dependent noise model for CMOS image sensors and achieved satisfactory results. The other is based on calculation from single image. The latter is broadly classified into two categories viz. The latter is broadly classified into two categories viz. methods based on variance stabilization transformation (VST) and methods based on fitting sample pairs
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