In this article, the authors propose an adaptive frequency median filter (AFMF) to remove the salt and pepper noise. AFMF uses the same adaptive condition of adaptive median filter (AMF). However, AFMF employs frequency median to restore grey values of the corrupted pixels instead of the median of AMF. The frequency median can exclude noisy pixels from evaluating a grey value of the centre pixel of the considered window, and it focuses on the uniqueness of grey values. Hence, the frequency median produces a grey value closer to the original grey value than the one by the median of AMF. Therefore, AFMF outperforms AMF. In experiments, the authors tested the proposed method on a variety of natural images of the MATLAB library, as well as the TESTIMAGES data set. Additionally, they also compared the denoising results of AFMF to the ones of other state-of-the-art denoising methods. The results showed that AFMF denoises more effectively than other methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0398

Adaptive frequency median filter for the salt and pepper denoising problem

The total variation (TV) regularization model for image restoration is widely utilized due to its edge preservation properties. Despite its advantages, the TV regularization can obtain spurious oscillations in flat regions of digital images and thus recent works advocate high-order TV regularization models. In this work, we propose an adaptive image restoration method based on a combination of first-order and second-order total variations regularization with an inverse-gradient-based adaptive parameter. The proposed model removes noise effectively and preserves image structures. Due to the adaptive parameter estimation based on the inverse gradient, it avoids the staircasing artifacts associated with TV regularization and its variant models. Experimental results indicate that the proposed method obtains better restorations in terms of visual quality as well as quantitatively. In particular, our proposed adaptive higher-order TV method obtained (19.3159, 0.7172, 0.90985, 0.79934, 0.99838) PSNR, SSIM, MS-SSIM, F-SIM, and P-SIM values compared to related models such as the TV-Bounded Hessian (18.9735, 0.6599, 0.8718, 0.73833, 0.99767), and TV-Laplacian (19.0345, 0.6719, 0.88198, 0.75405, 0.99789).

An adaptive method for image restoration based on high-order total variation and inverse gradient

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%.

/pdf/a-two-stage-filter-for-high-density-salt-and-pepper-4jd0koci1v.pdf

A Two-Stage Filter for High Density Salt and Pepper Denoising

https://doi.org/10.1016/j.bspc.2022.103552

Surya Prasath

Papers

Adaptive total variation L1 regularization for salt and pepper image denoising