Abstract
Noise estimation is fundamental and essential in a wide variety of computer vision, image, and video processing applications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the setting of noise levels. This paper proposes a new superpixel-based framework associated with statistical analysis for estimating the variance of additive Gaussian noise in digital images. The proposed approach consists of three major phases: superpixel classification, local variance computation, and statistical determination. The normalized cut algorithm is first adopted to effectively divide the image into a set of superpixel regions, from which the noise variance is computed and estimated. Subsequently, the Jarque–Bera test is used to exclude regions that are not normally distributed. The smallest standard deviation in the remaining regions is finally selected as the estimation result. A wide variety of noisy images with various scenarios were used to evaluate this new noise estimation algorithm. Experimental results indicated that the proposed framework provides accurate estimations across various noise levels. Comparing with many state-of-the-art methods, our algorithm strikes a good compromise between low-level and high-level noise estimations. It is suggested that the proposed method is of potential in many computer vision, image, and video processing applications that require automation.
Highlights
In the field of computer vision, signal, image, and video processing, noise is inevitable during data acquisition and transmission
Rather than estimating the noise level globally in the entire image, this paper proposes to classify the image into several subregions and compute the noise variance locally in each individual region to minimize the influence caused by color, texture, and lighting changes [1, 16, 17, 24]
To assess the performance quantitatively, the relative error in terms of the standard deviation was computed as given in the following equation: εr jσe −σ a j σa where σe represents the standard deviation of the estimated noise, σa represents the standard deviation of the added noise, and εr represents the relative percentage error between the added and estimated noise levels
Summary
In the field of computer vision, signal, image, and video processing, noise is inevitable during data acquisition and transmission. Image noise having a Gaussian-like distribution is quite often encountered, and it is characterized by adding to each pixel a random value obtained from a zero-mean Gaussian distribution, whose variance determines the magnitude of the corrupting noise. This zero-mean property enables such noise to be removed by locally averaging neighboring pixel values [10, 11]. Estimation for the amount of noise is critical in these methods, because it enables the process to adapt to the level of noise rather than using fixed values and thresholds. Existing noise estimation algorithms can be broadly classified into three major categories: filtered-based, block-based, and transform-based approaches [4, 5, 11, 16, 17]
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