Abstract
Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.
Highlights
Image denoising is the reconstruction of an original image from noisy observations gathered by a digital camera sensor without affecting critical features such as edges, textures, and singularities in the image
The noise reduction is mainly adopted by joint bilateral filter with the clearer reference image, which is made by Hard-Thresholding and RAISR
The peak signal-to-noise ratio (PSNR) and SSIM are used as a quantitative metric for performance evaluation
Summary
Image denoising is the reconstruction of an original image from noisy observations gathered by a digital camera sensor without affecting critical features such as edges, textures, and singularities in the image. During image acquisition and transmission, digital images are frequently contaminated by various types of noise. Some common noises encountered in digital images in the real world are Gaussian noise, Impulse noise, Poisson noise, Speckle noise, etc. Gaussian noise is a statistical noise having a probability density function equal to that of the normal distribution. It is usually caused by the thermal motion of electrons in the camera sensor while taking digital images. The goal of image denoising is to recover a clean image from a noisy measurement, z = x+n (1)
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