What is image deblurring?

What is image deblurring?

Thu Nov 24 2022

Image degradation is one of the most significant issues in image processing. Image blur is an undesirable reduction in bandwidth that lowers image quality and is difficult to avoid. Blur is caused by atmospheric instability as well as incorrect camera settings. Noise, in addition to blur effects, distorts the captured image. Image deblurring is a technique for removing blur from a degraded image and restoring the original image.

Blur can take numerous forms, such as Gaussian blur, motion blur, and so on. To deblur a degraded image, various techniques, and solutions have been presented in recent years. There are specific methods for removing different types of blurs. Image restoration has applications in a wide range of fields, including medical imaging, crime analysis, and astronomy. In this article, we will discuss several image deblurring techniques based on artificial intelligence and their performance analysis.

Why are images blurry?

Why are images blurry

Blurring always occurs during the capture of a digital image because scene information "spills over" to surrounding pixels.

Some blurring occurs within the camera:

  • The optical system of a camera's lens might be out of focus, smearing out the incoming light.

  • Because the lens is imperfect, light beams of different wavelengths take slightly different trajectories.

Other sources of blurring include:

  • movement of the camera or an object during the exposure.

  • light scattering distortion

  • insufficient depth of field

  • lens softness

  • In astronomical imaging, all incoming light in the telescope is slightly bent by atmospheric turbulence.

Blur classification

Images are becoming a significant part of our lives, sometimes in scientific applications or social networks, and image blurring may occur whenever there is an image. Blurred images are a key source of image distortion and degrade image quality. There are many types of blurs, like: motion blur, gaussian blur, average blur, and defocus blur.

Motion blur

motion blur

Motion blur is a common type of camera shake that occurs when your camera's shutter speed is too slow for you to hand-hold. It can also occur if you have unsteady hands and are unable to place the camera on a tripod. When an item seems blurry because it's moving too fast for the camera to capture a clear image, this is referred to as "motion blur." To better understand motion blur, we must first examine how cameras function.

Cameras, like our eyes, record the light that reflects off of things in our environment. However, there are two significant differences:

  • Cameras feature lenses that can be adjusted to control the quantity of light that reaches the film or sensor.

  • Cameras have the ability to catch light for longer periods of time than human sight.

Gaussian blur

gaussian blur

The result of a blurred Gaussian function image was Gaussian blur. It was commonly used in graphics applications, generally to reduce image blur. It is also applied as a pre-processing step in computer vision algorithms to improve image structures at multiple levels. This form of blurry effect is caused by a kernel that progressively gathers a particular number of pixels while following a bell-shaped curve. blanketed in the center with feathers on the edge.

Average blur

The image is convolved using a box filter during the average blur. The core element of the image is replaced in this operation by the average of all the pixels in the kernel area.

Defocus blur

Defocus blur

Defocus blur is one form of blur degradation caused by defocus and incorrect depth of focus. However, in scenarios with numerous depth layers, only the layer on a focal plane will concentrate on the camera sensor, leaving the others out of focus. This phenomenon can occasionally enhance the expressiveness of a photograph, but in most situations, it results in the loss of texture details or unintelligible information.

What is image deblurring?

Image deblurring is a type of image restoration that focuses on restoring clean images by removing distortions. Blurring artifacts are one type of distortion. Deblurring is the process of recovering a clear image from a blurred input. Because it is intrinsically an inverse issue, it does not have a single unique solution; it's therefore an ill-posed problem. For decades, image deblurring online has been studied.

Why is image deblurring important?

It is so important because it allows us to extract maximum information in circumstances where obtaining an image without blur is expensive or even impossible, such as astronomical photographs and medical images. It also has practical applications: for example, bar-code readers for use in shops and shipping businesses must be able to correct for flaws in the scanner optics. Important biometric applications include iris or retinal scanning and fingerprint identification.

Why is image deblurring important?

Algorithms for image deblurring

There are numerous image deblurring algorithms, from classic methods that mathematically estimate the blur kernel and then reverse its effect; to more recent machine learning as a service based methods that benefit from recent advances in machine learning and deep learning as a service.

The options for image deblurring at Saiwa

The options for image deblurring at Saiwa

We provide two image deblurring options at SAIWA, one classic and one deep learning based: prior-based blind deblurring and multi-stage progressive image restoration network (MPRNet).

Prior-based blind deblurring

It is a technique for blind kernel estimation and deblurring based on the l0 gradient prior. The method begins by estimating the blur kernel by alternating between a sharp image prediction using the l0 prior on the gradient picture and a multi-scale kernel estimation. after estimating the kernel, a crisp picture is projected using a typical non-blind deconvolution algorithm using the previously determined kernel. 

Multi-stage progressive image restoration network (MPRNet)

MPRNet is a CNN (convolutional neural network) with three stages for image restoration. MPRNet has been demonstrated to provide high performance benefits on a variety of datasets for image restoration challenges such as image deraining, deblurring, and denoising.

The three-stage structure of MPRNet shown in following figure provides several key features:

  1. In the first two stages, an encoder-decoder for learning multi-scale contextual information.

  2. In the final stage, the original image resolution is used to preserve precise spatial characteristics of the input image.

  3. A supervised attention module SAM that allows for progressive learning.

  4. Cross-stage feature fusion (CSFF) is a technique for propagating multi-scale contextualized features from the early to late stages.

Multi-stage progressive image restoration network (MPRNet)

The advantages of image deblurring at Saiwa

  • Providing both classic and deep learning methods: a deblurring approach based on blind kernel estimation and a deep learning method

  • Adjusting parameters to test the classic method on images with various kernel sizes.

  • Image aggregation enabling the method to be applied to several images at once.

  • View and save the resultant images.

  • Exporting and archiving findings on the user's cloud or locally.

  • Saiwa team service customization using "Request for customization" option.

Why are image deblurring methods employed?

Because Image deblurring is a procedure that uses a mathematical model to make images sharp and usable. Image deblurring has a wide range of applications, from consumer photography to radar imaging and tomography, where it removes the effect of the imaging system response.


Image deblurring and related techniques are vital for both the advancement of modern photography and the restoration of images and movies that are not as clear as they could be. The ability to remove noise from pictures collected in highly specialized fields such as astronomy and medicine is vital to improving the associated experts' ability to perform their professions as effectively as possible. This demonstrates the significance of performing research in both academic and industrial settings, to improve the accuracy and sophistication of image deblurring algorithms.

Despite the fact that our investigation was confined to a few methods of deblurring, the results demonstrate the importance of image deblurring techniques and justify additional research into more complex approaches. More image deblurring techniques would undoubtedly be valuable in obtaining a better understanding of the problems and algorithms involved in image deblurring.

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