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A Brief Review of Image Denoising Techniques

Before applying any image-denoising technique, one should understand its limitations. A single method may only be effective in some circumstances, and the result may be unsatisfactory, especially if the image has high noise. This article will discuss variational, spatial domain, non-linear methods, and SUREShrink.

Variational denoising

Variational image denoising techniques aim to improve the quality of image data by reducing noise. This task’s key is finding the best method for the image in question. This can be achieved by combining several different methods. Here, we will focus on a method that can be applied to medical images.

Several approaches to image denoising have been proposed in the last few decades. Some are the TV model, LLT model, and fractional image denoising technique. The TV model preserves the tissue margin, while the LLT model prefers smooth solutions. The proposed model can handle both smooth and non-smooth image-denoising problems. It uses the Split-Bregman algorithm to validate the theoretical results.

Spatial domain methods

Image denoising is an integral part of image processing. The goal of denoising is to increase the peak signal-to-noise ratio. This can be achieved through a variety of methods. Image denoising algorithms differ in their computational complexity and the quality of the denoised images.

Image denoising is essential in many applications, such as remote sensing. Noise affects the quality of remote-sensing images and can decrease the accuracy of analyses. This study evaluated the effectiveness of different denoising methods for different spatial resolutions.

Non-linear methods

Image denoising is an essential operation in image processing. It is a fundamental task and holds practical relevance in various real-world applications. Non-linear methods have dominated the field of denoising algorithms, which explicitly exploit the self-similarity of patches within the targeted image. In recent years, however, discriminatively trained local denoising algorithms have begun to outperform non-linear models in performance and computational efficiency. These methods include a cascade of shrinkage fields and trainable non-linear reaction-diffusion.

Local methods are limited by their performance, especially when the noise level is high. High noise levels severely disturb the correlations between neighboring pixels. Non-linear image denoising methods can effectively compute the best solution for a given image but have three significant drawbacks. The first is that textures tend to be over-smoothed. In addition, flat areas are approximated by piecewise continuous surfaces, which can result in a stair-casing effect. Another disadvantage of this technique is that it is inefficient in retaining contrast.


Image denoising technique based on singular value iteration contraction (SVIC)is used to eliminate noise in images. The BayesShrink algorithm calculates the original image’s mean square error (MSE) and the denoised estimate image.

It combines the advantages of traditional and modern image-denoising techniques to improve the quality of images. Unlike conventional methods, the proposed algorithm uses a single threshold to estimate the neighboring wavelet detail coefficients and has a high probability of removing additive noise. This method can be used to reduce the noise in images that have a large amount of noise.

TV-based regularization model

TV-based regularization models have several advantages over their conventional counterparts. They can recover low-frequency parts of an image faster than sharp edges and use physically tractable constraints, which accelerate convergence. Here, we discuss some advantages and drawbacks of this method.

Adaptive TV/L2-based image denoising algorithms can be improved through automatic parameter selection. This approach is based on the close relationship between the objective function and the regularization parameter. This method can be applied to different SNR environments. The objective function and parameter are dynamically updated, and the updating model applies the Morozov discrepancy principle to model the convex function concerning the regularization parameter.

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