Image Enhancement is the process of improving the quality of a picture without losing information to obtain the desired visual result. Image quality enhancement usually includes a number of transformations: noise reduction, improving a blurry photo, increasing resolution, contrast, brightening a dark photo, removing optical distortions, etc. You may need special tools to process photos. The best apps are presented on appslikethese website.
Image post-processing is an integral and important part of the photo creation process. It is needed to eliminate common flaws in photos, including:
- low contrast;
- backlighting of the subject;
- improper color balance;
- defocus, and more.
Popular photo editors, such as Adobe Photoshop, Adobe Lightroom, or RawTherapee, offer various sets of tools for retouching images and have long been leaders on the market. Nevertheless, the quality of the processing result strongly depends on the retouchers’ skills and aesthetic perception, which is subjective. In addition, manual editing of digital photos usually takes a lot of time.
Advantages of automatic photo processing based on AI
Modern services for photo autocorrection make the process of retouching much easier and simpler for all users. AI-based photo editors do everything the retoucher would do manually in photoshop, and at the same time allow you full control over the process. Automatic retouching helps:
- give images taken with a simple camera a professional look and improve image quality;
- save time by letting the software do all the work instead of editing manually;
- help printing and publishing companies to improve the process of color correction of images for magazines, marketing campaigns, and much more.
Image enhancement algorithms are not limited to digital photography. They are used to process medical images, for example, in radiology, where AI not only helps improve the quality of the image itself but can also detect certain diseases.
“Smart” image enhancement yields good results that can later be used in various computer vision systems:
- infrared image enhancement is critical for Smart City applications;
- forensic video and photo processing software, such as Amped FIVE, allows you to restore the outlines of a car in motion, measure objects in crime scene photos, detect faces in blurry/dark photos, improve the clarity of text in photos – we have seen this in FBI and CSI movies;
- when you need to sharpen photos in haze and fog – can be used to interpolate and measure the distance between objects under different atmospheric conditions in the development of visual systems for unmanned vehicles.
How a neural network improves photos. Examples of open source solutions
Deep Learning is a relatively new area of Machine Learning and can be effectively applied to image processing. Different types of neural networks can be used for various tasks to improve the image quality, such as removal of noise, reconstruction of high-resolution images from low-resolution image data (super-resolution).
We have selected several interesting solutions and will tell you how the deep neural network photo-editor will help you to automatically improve the quality of pictures.
Deep Photo Enhancer – improve poor quality pictures with neuronics
Deep Photo Enhancer uses GAN (Generative Adversarial Networks) and unpaired learning algorithms. Deep Photo Enhancer offers an image enhancement method based on photo learning. Neurons learn to find common characteristics in a set of sample images (e.g., contrast level, white balance, color gamut) and then apply these characteristics to the enhanced image so that the meaning of the original picture is preserved. This method requires the use of original images of high quality and can be further personalized.
Super-resolution photos using Neural Enhance
Deep learning algorithms can teach a neural network to magnify images by a factor of 2 or even 4, which improves the quality of low-resolution photos. Deep neural networks (GAN, deep recursive convolutional neural networks (DRCN)) are able to reconstruct photorealistic textures from very low-resolution images. The neural network “finishes” the details based on training results on sample images. This is not a 100% accurate reconstruction of photos in HD, but the results are impressive.
IBM/MAX Image Resolution Enhancer – a neural network for compressed photo reconstruction
This deployable model allows increasing the size of a pixelated image by 4 times, while generating photorealistic details, using GAN trained on 600 thousand OpenImages V4 images. The ideal input image should be a PNG file of 100×100 to 500×500 pixels, preferably without post-processing. The model can generate detail from a pixelated image, but it is not suitable for correcting blurred images.
In lieu of a conclusion
AI-based automatic image processing tools using deep learning algorithms can help you in developing intelligent applications, implementing computer vision elements, detecting and recognizing objects and actions in images and videos.