What is image denoising?
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
- 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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