What is the purpose of image preprocessing in deep learning?

"Garbage in, garbage out.” This concept common to computer science and mathematics in computer science and machine learning points out a very important point: even the best computer vision models will perform badly if the input data is of low quality. It is always crucial to work toward gathering high-quality data for the current task. Additionally, even with high-quality data, preprocessing enables the best outcomes to be attained.

What is image preprocessing?

Before being used for model training and inference, pictures must first undergo image preprocessing. This includes, but is not limited to, adjustments to the size, orientation, and color. The purpose of pre-processing is to raise the image's quality so that we can analyze it more effectively. Preprocessing allows us to eliminate unwanted distortions and improve specific qualities that are essential for the application we are working on. Those characteristics could change depending on the application. An image must be preprocessed in order for software to function correctly and produce the desired results.

Why is it important?

To prepare picture data for model input, preprocessing is necessary. For instance, convolutional neural networks' fully connected layers demanded that all the images be in arrays of the same size.

Additionally, model preprocessing may shorten model training time and speed up model inference. If the input images are very huge, shrinking the size of these images will greatly decrease the amount of time needed to train the model without significantly affecting model performance. 

Even though geometric transformations of images (like rotation, scaling, and translation) are categorized as pre-processing techniques, the goal of pre-processing is an improvement of the image data that suppresses unintentional distortions or enhances some image features crucial for subsequent processing.

What preprocessing steps should be used? 

1. Orientation:

When a picture is taken, its metadata informs our computers how to show the input image in relation to how it is stored on disk. Its EXIF orientation is the name given to that metadata, and incorrect EXIF data handling has long been a source of frustration for developers everywhere. This also holds true for models: if we've established annotated bound boxes on how we viewed an image to be orientated but our model is "seeing" the picture in a different orientation, we're utterly mistraining the model.

2. Resize:

Although altering an image's size may seem simple, there are things to keep in mind. Few devices capture exactly square images, despite the fact that many model topologies require square input images. Stretching an image's dimensions to make it square is one option, as is maintaining the image's aspect ratio while adding additional pixels to fill in the newly formed "dead space."

3. Random Flips:

Our model must acknowledge that an object need not always be read from left to right or up to down by randomly reflecting it about its x- or y-axis. In order-dependent circumstances, such as when deciphering text, flipping may be irrational.

4. Grayscale:

One type of image transformation that can be applied to all images (train and test) is a change in color. Random changes can also be made to images only during training as augmentations. Every image is often subjected to grayscaling, which alters the color. While we may believe that "more signal is always better, we may actually observe more timely model performance when images are rendered in grayscale.

5. Different Exposure:

‍If a model might be expected to operate in a range of lighting conditions, changing image brightness to be randomly brighter and darker is most appropriate. The maximum and minimum levels of brightness in the space must be taken into account.

Real world applications of image preprocessing in machine learning.

1. Medical imaging: Image processing can provide sharp, high-quality images for scientific and medical studies, ultimately assisting doctors in making diagnoses.

2. Military and defense: Steganography is an intriguing use of Image Processing in Defense. In order to communicate information back and forth without a third party noticing the message, experts can conceal a message or an image inside another image.

3. The most common application of image processing is arguably general image sharpening and restoration. Using Photoshop or other image editing software to enhance and manipulate photos, or applying creative effects to our photos on Snapchat or Instagram.

Image Processing techniques used in the ML industry

In order to transform images for a variety of tasks, such as applying artistic filters, adjusting an image for best quality, or enhancing particular image details to maximize quality for computer vision tasks, many advanced image processing techniques use machine learning models like deep neural networks.

Convolutional Neural Networks (CNN) learn to do tasks like object detection, image segmentation, and classification by taking in an input image and applying filters to it.

Recent advances in machine learning enable engineers to enhance image data in addition to doing image alteration. 

Conclusion 

Image operations at the most basic level of abstraction are referred to as image pre-processing. If entropy is used as a measure of information, these processes actually reduce the amount of information present in the image. Pre-processing aims to improve the picture data by enhancing certain image features that are important for subsequent processing and analysis tasks or suppressing undesirable distortions.