Useful and Effective Image Denoising online tool

 As digital cameras and cellphones become more prevalent in people's lives, they encounter a wide range of images. While some of these images are of good quality, others suffer from noise, which diminishes their quality. Noise in images can be caused by factors such as low light levels and intensity issues. Various methods are available online for image denoising online or reducing noise in an image. Image Denoising online has been an important area of research for a long time, and experts are still studying it. In this section, we will examine how deep learning techniques are used for image denoising.

What is Noise?

In daily life, people encounter a variety of images due to the increasing use of digital cameras and cellphones. However, the quality of these images can be affected by the presence of noise, which is a random change in brightness or color caused by technological limitations or poor environmental conditions during image capture. To address this issue, various techniques have been developed to reduce image denoising online, which is often unavoidable in real-world situations. The amount of noise in an image is determined by the number of damaged pixels, and several factors can contribute to its introduction during acquisition and transmission.

Sources of Noise

The presence of noise in an image can occur during the process of capturing or transmitting the photo. There are various factors that can contribute to the occurrence of noise in a picture, and the number of damaged pixels in the image is an indicator of the degree of noise present. Image noise can vary widely, from barely noticeable specks in an image captured in ideal lighting conditions to images such as optical or radio astronomical images that are predominantly noise, making it difficult to discern the subject without extensive processing. This level of noise is generally considered unacceptable in an image.

 

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

Noise in digital images can be classified into various types based on the characteristics of the source. Gaussian noise is a common type of noise that results from random fluctuations in the image sensor's sensitivity or from issues with the electronic circuitry. Impulse noise is another type of noise that appears as randomly scattered pixels with very high or very low intensity values, and it often arises due to faults in the sensor or the transmission process. Periodic noise is a repeating pattern that appears in the image due to electrical interference or a fault in the image sensor. Finally, banding noise appears as horizontal or vertical stripes across the image and can be caused by uneven illumination or a defect in the sensor's readout circuitry

Gaussian noise

Gaussian noise is a type of random noise that is commonly seen in digital images. It arises due to sensor limitations during image capture, particularly in low-light situations where visible light sensors struggle to efficiently capture scene information. Gaussian noise is characterized by a statistical distribution called the normal probability density function. This type of noise is named after Carl Friedrich Gauss, a German mathematician who made significant contributions to the study of probability theory. 

impulse noise

mpulse noise is usually caused by defects in the hardware or the camera sensor, which corrupts images by replacing some pixels in the original image. There are two types of impulse noise. The first type is a salt-and-pepper noise, where some pixels take on only one of two extreme values (black or white). The second type is random-valued impulse noise, where some pixels can have any value within the range of [0-255].

periodic noise

I apologize for my previous response. I made a mistake in my understanding of periodic noise. Periodic noise is actually a type of noise that appears in images as a result of regular patterns or structures in the scene being photographed, or as a result of imperfections in the camera sensor or lens. This type of noise appears as repetitive variations in the brightness or color of an image. Examples of periodic noise include moiré patterns and banding.

Periodic noise can be difficult to remove because it is often correlated with the underlying structure of the image, and traditional denoising techniques such as filtering may not be effective. However, advanced techniques such as wavelet-based image denoising online or deep learning-based denoising have been shown to be effective in removing periodic noise from images.

banding noise

Banding noise is a type of image noise that is caused by the camera's digital sensor when it receives data. This noise is specific to each camera and can be seen as bands or stripes in the image. It is often more noticeable in the shadows or in areas where the image has been brightened or when high ISO settings are used. The appearance of banding noise can also vary depending on the white balance used.

What is image denoising online?

Image denoising online is a process of removing a specific type of distortion - noise - from images, and it falls under the category of image restoration. Noise can be described as unwanted variations in brightness or color information that appear randomly. It is an inevitable consequence of image capture, compression, and transmission, and its level can increase with exposure time, temperature, and camera sensitivity. Image denoising online has been extensively studied, but it remains a challenging problem because it is an inverse problem and therefore lacks a unique solution. Classic image denoisers aim to model image noise mathematically, while recent solutions use machine learning and deep learning techniques to denoise images.

Image denoising online at Saiwa

There are many options to denoising images 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

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 online 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

 

• Less 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