What Is Image Denoising ?
People are encountered a variety of different images in their daily lives as the use of digital cameras and cellphones grows. Some of the images are of good quality, whereas others aren't. The presence of noise reduces image quality. This noise might be generated by low light levels or other intensity issues. There are several methods for image denoising, or reducing the noise in an image. It has been a significant point of study for a long time, and it is still being studied by specialists.
In this article, we will look at how artificial intelligence techniques are used to denoise an image.
What is Noise?
Noise is commonly defined as a random change in brightness or color information, and it is generally caused by the technological limitations of the image capture sensor or by poor environmental conditions. Image noise is a common issue that must be addressed using effective image denoising techniques because it is unavoidable in real-world situations.
Noise may be generated in the image during acquisition and transmission. Several causes might contribute to the introduction of noise into the image. The quantity of damaged pixels in the image determines the quantification of noise.
Sources of Noise
Noise may be generated in the image during photo collection and transmission. Several causes might contribute to the presence of noise in the picture. The number of damaged pixels in an image determines the quantification of noise.
Image noise can range from practically imperceptible specks on a digital image taken in perfect lighting to optical and radio-astronomical images that are nearly entirely noise, from which a small amount of data can be extracted by complicated processing. Such a level of noise would just be unacceptable in an image since it would be difficult to recognize the subject.
The principal sources of noise in digital images are as follows:
The imaging sensor may be affected by environmental conditions.
Image noise can be caused by low light and sensor temperatures.
Noise in the digital image might be caused by dust particles in the scanner.
Interference in the transmission channel
Noise Classification
The noise is distinguished by its pattern as well as its probabilistic features. There are several types of noise depending on the source, including Gaussian noise, impulse noise, periodic noise, and banding noise.
Gaussian noise
Gaussian noise occurs in digital imaging as a result of sensor constraints during image collection in low-light circumstances, which make it difficult for visible light sensors to capture scene information efficiently. Gaussian noise is a random statistical noise having a normal probability density function.
Periodic noise
Periodic noise is indeed an unwanted signal that, depending on its source, interferes with the source image or signal at a random frequency. In general, this interference might come from natural sources, the electricity network, or technological equipment.
Banding noise
Banding noise is camera-dependent noise that occurs when the camera receives data from the digital sensor. Banding noise is usually noticeable at high ISO settings, in the shadows, or when an image is over brightened. Depending on the camera type, banding noise may also increase for specific white balances.
Impulse noise
Typically, impulse noise corrupts images as a result of a defect in the device's hardware or the camera's sensor. Some pixels in the original image are replaced by impulse noise. The first form of impulse noise is salt-and-pepper noise, which takes just one of the two values [0,55]. The other form is random-valued impulse noise, which can have any value between [0-255].
What is image denoising?
Image denoising is a sub-category of image restoration that is about restoring clean images by removing a specific kind of distortion: noise. "Noise" refers to the random appearance of undesired traces and variations in the brightness or color information. Noise inevitably contaminates images during capture, compression, and transmission. The level of noise typically increases with the length of exposure, physical temperature, and sensitivity setting of the camera.Image denoising has been studied for a long time, and yet it remains a challenging and open problem because image denoising is an inverse problem and therefore does not have one unique solution. There are wide range of classic image denoisers that try to model image noise mathematically, and there are also many recent solutions motivated by recent advances in machine learning and deep learning as a service.
Image denoising transform methods
The first approaches to image denoising were in the spatial domain, while the most recent methods are in the transform domain. Originally derived from the Fourier transform, transform domain approaches have grown to include a number of techniques, including the cosine transform, wavelet domain methods, block matching and 3D filtering (BM3D), among others. The properties of image information and noise are different in the transform domain, an observation that is exploited by transform domain approaches.
Transform domain filtering methods
Unlike spatial domain filtering techniques, transform domain filtering techniques first transform the noisy input image into another domain and then apply an image denoising technique to the transformed image according to the different characteristics of the input image and its noise (larger coefficients denote the high frequency part, such as the details or edges of the image, while smaller coefficients denote the noise). The selected basis transform functions, which can be data-adaptive or non-data-adaptive, can be used to further categories the transform domain filtering techniques.
Data adaptive transform
Independent Component Analysis (ICA) and PCA functions are used as transform techniques on the provided noisy images. Among them, the ICA approach has been effectively used for degaussing non-Gaussian data. The assumptions regarding the distinction between image and noise still apply to these two types of data-adaptive algorithms. However, because they use sliding windows and require a noise-free data sample or at least two frames from the same scene, their main drawback is high computational complexity. Nevertheless, in some applications it may be difficult to obtain noise-free training data.
Non-data adaptive transform
The two domains of non-data adaptive transform domain filtering techniques are the spatial frequency domain and the wavelet domain. Low-pass filtering is a technique used in spatial frequency domain filtering methods where a frequency domain filter is created that passes all frequencies below a cut-off frequency and attenuates all oscillations above the cut-off frequency. Image data often expands in the lower frequency domain after being transformed by low-pass filters such as the Fourier transform, whereas noise typically expands in the high frequency domain. So, by selecting certain characteristics of the transform domain and translating them back into the image domain, we can eliminate noise.
BM3D
The most widely used image denoising technique, BM3D, was presented by Dabov et al. as a powerful and efficient extension of the NLM method. In the transform domain, BM3D is a two-stage non-local collaborative filtering technique. Using block matching, this technique stacks related patches into 3D groups, which are then transformed into the wavelet domain. Hard thresholding or coefficient-based Wiener filtering is then applied in the wavelet domain. Finally, all the estimated patches are combined to reconstruct the overall image using an inverse transform of the coefficients. However, as the noise level steadily increases, BM3D's denoising effectiveness decreases significantly and artefacts begin to appear, particularly in flat regions.
Image denoising at Saiwa
There are many options to but in Saiwa, we provide two image denoising online options, one classic and one deep learning based: Multi-Scale DCT Denoiser and multi-stage progressive image restoration network (MPRNet)
Multi-Scale DCT Denoising
Multi-Scale DCT Denoising is a classic denoising algorithm with low computational complexity. The original DCT denoising algorithm starts by thresholding of a patch-wise Discrete Cousin Transform (DCT) of the noisy input image and then aggregation of the resulting patches. There are variants of DCT denoising. In a successful attempt a two-step multi-scale version is proposed in. that enhances the performance of the original method significantly and also reduces halo artifacts in the denoised image.
The main advantages of the Multi-Scale DCT denoiser
A multi-scale version of DCT that keeps all features of its single scale while improving its performance.
An extra guide image (or oracle), which is a first denoised image to estimate the empirical Wiener factors of the DCT coefficients in the second step.
Adaptive patch aggregation that reduces the halo effects around the contrasted image edges.
Multi-stage progressive image restoration network (MPRNet)
MPRNet is a CNN (convolutional neural network) with three stages for image restoration. MPRNet has been established to provide significant performance gains on several datasets for a variety of image restoration problems such as image deraining, deblurring, and denoising.
The three-stage structure of MPRNet shown in following figure provides several key features:
An encoder-decoder for learning multi-scale contextual information in the first two stages.
Preservation of fine spatial details of the input image by operating on the original image resolution in the last stage.
A supervised attention module (SAM) that enables progressive learning.
Cross-stage feature fusion (CSFF) to propagate multi-scale contextualized features from early to late.
What is Denoising in Image Processing in Low-Light Conditions
Low-light imaging scenarios pose significant challenges for image denoising techniques. In low-light conditions, the signal-to-noise ratio (SNR) of the captured images is typically low, resulting in significant noise levels. To answer what is denoising in image processing we must know traditional denoising methods may struggle to effectively remove noise while preserving important image details and textures in such scenarios. Deep learning-based denoising techniques have shown promising results for low-light image denoising by leveraging their ability to learn complex noise patterns and image statistics from large datasets. However, these methods often require specialized training data and may not generalize well to unseen low-light conditions or noise distributions.
Researchers are exploring various strategies to improve low-light image denoising, including the use of specialized network architectures, transfer learning from high-quality datasets, and integration with other image enhancement techniques such as super-resolution and HDR imaging.
Challenges and Limitations of Image Denoising Techniques
Despite significant advances in image denoising, several challenges and limitations persist. One major challenge is the trade-off between noise removal and preservation of essential image features, such as edges and textures. Overly aggressive denoising can result in loss of important details and blurring of the image, while insufficient denoising may leave undesirable noise artifacts.
Another challenge is the presence of diverse noise types and varying noise levels in different imaging scenarios. To know what is denoising in image processing you can consider there are many denoising techniques are optimized for specific noise models (e.g., Gaussian noise, salt-and-pepper noise) and may perform sub-optimally when faced with different noise distributions or mixed noise types. Additionally, the computational complexity of some denoising techniques, particularly those based on deep learning, can be a limiting factor in real-time or resource-constrained applications. Striking a balance between denoising performance, computational efficiency, and memory requirements is an ongoing challenge.
Image denoising applications
image denoising can be used as a pre-processing stage in any image processing application that works with input noisy images. Furthermore, this technique has several applications, the most significant of which are as follows:
Image processing in medicine
Application of industrial machine vision
Imaging astronomy
Machine vision systems' pre-processing stage
Conclusion
There will be numerous images that must go through the distillation process to extract as much information as possible, regardless of practice or precise capture. We've examined common types of noise and their importance in this situation. We've also discussed how deep learning can be used to denoise images.