What is image denoising?

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As the usage of digital cameras and cell phones develops, people are exposed to a wider range of images in their daily lives. Some of the photographs are high-quality, while others are not. Image quality suffers when there is noise present. This noise might be caused by low light levels or other difficulties with intensity. There are various online ways for picture denoising or image denoising. It has long been a prominent area of study, and it is currently being researched by professionals. We will look at how deep learning algorithms are used to denoise a picture in this part.

What is Noise?



Noise is widely characterized as a random shift in brightness or color information, and it is typically produced by the picture capture sensor's technological limits or bad environmental circumstances. Image noise is a prevalent issue that must be handled with good image-denoising algorithms since it is inescapable in real-world circumstances.

During picture acquisition and transmission, noise may be created. Several factors might lead to the presence of noise in the picture. The quantification of noise is determined by the number of damaged pixels in the image as well as image denoising technique is one the best solution for solving this issue

Sources of Noise

During image capture and transmission, noise may be introduced into the image. Several factors might lead to the presence of noise in the image. The measurement of noise is determined by the number of broken pixels in a picture.

Image noise can range from almost invisible specks on a digital image captured in perfect illumination to essentially totally noisy optical and radio astronomical photos from which a little amount of data can be retrieved through sophisticated processing. Such a degree of noise in a photograph would simply be unacceptable since it would make it difficult to identify 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

 

Different Types of Noise

The pattern of the noise in addition to its probabilistic properties separates it. Depending on the source, there are numerous forms of noise, including Gaussian noise, impulse noise, periodic noise, and banded noise.

Gaussian noise



Gaussian noise happens in digital photography as a result of sensor restrictions during picture collection in low-light conditions, making it visible light sensors difficult to record scene information efficiently. Gaussian noise is a statistically random noise with a normal probability distribution function.

 impulse noise

In most cases, impulsive noise corrupts photos due to a flaw in the device's hardware or the camera's sensor. Impulse noise replaces certain pixels in the original picture. The first type of impulse noise is f, which can only take one of two values [0,55]. Random-valued impulse noise, on the other hand, can have any value between [0-255].

periodic noise



Periodic noise is an undesired signal that interferes with the source picture or signal at a random frequency, depending on its source. In general, this interference might be caused by natural sources, the electrical grid, or technological devices.

banding noise



Banding noise is camera-dependent noise that arises when data from the digital sensor is received by the camera. Banding noise is typically seen at high ISO settings, in the shadow, or when a picture is overexposed. Banding noise may also rise for various white balances based on the camera type.

 

What exactly is image denoising?

 


Image denoising is a sub-category of image restoration that focuses on recovering clean pictures by eliminating a particular type of distortion: noise. The random occurrence of undesirable traces and changes in luminance or color information is referred to as noise. Image contamination is unavoidable throughout capture, compression, and transmission. The noise level normally rises with the length of exposure, physical temperature, and sensitivity level of the camera. Image denoising has been studied for a long time, but it remains a difficult and open problem because image denoising is an inverse problem with no single solution.

 

Image denoising at Saiwa

There are many options for denoising images but in Saiwa we provide two image denoising 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 the aggregation of the resulting patches. There are variants of DCT denoising. In a successful attempt a two-step multi-scale version is proposed. 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

1. A multi-scale version of DCT that keeps all features of its single scale while improving its performance.

2. 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

3. 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 Figure 2 provides several key features:

1. An encoder-decoder for learning multi-scale contextual information in the first two stages

2. Preservation of fine spatial details of the input image by operating on the original image resolution in the last stage

3. A supervised attention module (SAM) that enables progressive learning

4. Cross-stage feature fusion (CSFF) to propagate multi-scale contextualized features from early to late stages.

 

 


 

Image denoising applications

Denoising images can be used as a pre-processing stage in any image processing application which 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

 

 

Advantages of Saiwa image denoising

  • Providing both classic and deep learning denoisers: fast low-complex non-blind denoiser and parameter-less blind deep learning denoiser
  •  Fewer halo artifacts
  • Parameter change to test the conventional technique on photos with varying noise standard deviations
  • Image aggregation allows the method to be applied to several photos at the same time.
  • View and download the resulting photos
  • Exporting and keeping the results in the user's cloud space or locally
  • Service customization by the saiwa team using the "Request for customization" option

 

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