Denoising Images with Deep Learning: High-Level Visual Tasks and Beyond

To be able to process images and visual inputs and understand visual information was only something humans could do until recently. Now, we have computer systems that can understand, comprehend, and interpret visual information and images.

But what happens when this visual information is of poor quality, grainy, noisy, or worse- blurry!

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Deep Learning has risen above many technologies to become a powerhouse of possibilities. From understanding visual information and learning from itself to predicting patterns based on a small sample set- deep learning has done it all.

How Deep Learning aids in denoising images?

In today’s time, Deep Learning has come to a point where it can process images to make them clearer, sharper, and more understandable. Whether it’s hazy captures or digital visualization that is grainy- deep learning can denoise images with high accuracy. Deep learning is now being increasingly deployed to remove grains for images and achieve sharper imagery.

Now, deep learning can, without human intervention, learn from itself and identify newer kinds of noise in the images to restore it to higher quality output.

Many kinds of deep learning algorithms can be used to achieve image denoising. To restore an image to its highest quality, deep learning is now being used. It helps to identify and retrieve relevant information from noisy images by minimizing the noise. This has been a huge challenge in the image processing industry in the past but the recent advancements of deep learning have not only made this possible but also feasible.

When images are captured, stored, or transmitted- many of them lose quality due to compressing and transfer.

Many activities that require high-quality images take a backseat due to noise in these images.

For example, tracking through computer vision, a vital activity that requires visual inputs can suffer due to noisy images. This might not affect just the quality of input data, but as a result, could also skew the output data and cause the computer vision system to misidentify information- which could be risky.

Photo by Glenn Carstens on Unsplash

Traditional methods vs Deep Learning

Traditional methods of image denoising often assume that the noise is evenly spread throughout the visual inputs. This is often not the case.

The images in the real world might be more complicatedly noisy than what traditional systems understand. Blind noise is the pollution that occurs in real pictures. This kind of pollution of images is complex and not evenly spread contrary to Gaussian noise. And blind denoising occurs when the deep learning system has to identify the noise, or the basis for denoising, from the image itself.

This means, that the deep learning system “sees” the noisy sample and identifies what it needs to fix.

Deep Learning-based algorithms can work overtime to improve the identification and removal of noise from data. When high-level computer vision tasks are performed on data that is noisy, it is always advisable to add image denoising as a step prior to data processing. This is because computer vision is highly observant and even a little noise in imagery could change the output.

In conventional methods of denoising images, many a time, details are lost due to over denoising. For example, textures and patterns of artefacts might be smoothened and blurred out.

But with convolutional neural networks, the data set improves on images through feature inputs that are up-sampled and down-sampled. This means, that when an image is processed, it is fused with the improved image on the next scale and the image on the lower scale- which helps in creating a framework of accurate restoration.

Photo by Jakob Owens on Unsplash

Denoising of Images: Uses

  1. To produce visually and aesthetically pleasing results through input data
  2. Attain visual accuracy to perform high-level vision tasks

There are many kinds of noise that might affect images. These are:

  1. Additive White Noise
  2. Blind Noise
  3. Real Noise
  4. Hybrid Noise

Machine learning models consist of different types of learning:

Supervised learning models are fed data sets and samples and use these features to obtain an outcome closer to the target.

Unsupervised learning models give outputs based on the training rather than the input data. They are used to find and identify patterns and use their training to identify the labels and then match the label outcome.

Semi-supervised ML models run on a hybrid of both supervised and unsupervised learning.

To effectively use deep learning for denoising images, it is important to ensure that the training data set and the test data set effectively include both grayscale and colored noisy images.

Deep learning techniques in today’s time are witnessing a rise in the process of reducing additive white noise in imagery.

All visual noise has different noise levels especially when it comes to image denoising. It becomes increasingly important to improve deep learning models to tackle all kinds of noise that might come in its way.

Also read: Deep Residual Learning For Image Recognition (tooli.qa)

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FAQs

Quick queries for this insight

How Deep Learning aids in denoising images?

Traditional methods of image denoising often assume that the noise is evenly spread throughout the visual inputs. This is often not the case. The images in the real world might be more complicatedly noisy than what traditional systems understand. Blind noise is the pollution that occurs in real pictures. This kind of pollution of images is complex and not evenly spread contrary to Gaussian noise. And blind denoising occurs when the deep learning system has to identify the noise, or the basis for denoising, from the image itself. This means, that the deep learning system “sees” the noisy sample and identifies what it needs to fix. with convolutional neural networks, the data set improves on images through feature inputs that are upsampled and downsampled. This means, that when an image is processed, it is fused with the improved image on the next scale and the image on the lower scale- which helps in creating a framework of accurate restoration.

Does unsupervised learning offer any advantages over supervised learning?

Unsupervised learning uses raw or unlabeled data to identify patterns and further make decisions based on the same. It is advantageous over supervised learning in the sense that the latter requires labelled input and output data during the training phase of the machine learning lifecycle, which in turn requires human insights resulting in a resource intensive method. But the applications of both methods vary. Whilst unsupervised learning could be used to perform initial data analysis, supervised learning could be used to classify different file types.

Why do deep learning methods offer better solutions for denoising than their traditional counterparts?

Traditional methods are based on the assumption that the noise is evenly spread throughout the visual input, which is not the case in reality. Deep learning methods, in turn, identify the noise and then remove the same, and hence achieve adaptive denoising.

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