De-aliasing

What is De-aliasing?

De-aliasing is a problem that arises while acquiring images. Sometimes, while acquiring an image, high-frequency information is lost, or rather, the original information gets distorted, and the image gets overexposed. So, the task of recovering the original high-frequency information and improving the image quality is known as de-aliasing. The technique is very popular in numerous areas, including computer vision, medical imaging, remote sensing, and many more.

Why do we need De-aliasing?

The primary purpose of performing de-aliasing is to improve the quality of the acquired image. When an image gets overexposed, the image loses its essence, and the viewers will not be able to get the required information from the image. In some cases, overexposure can be the reason for the image to be of no use. This is where de-aliasing comes in. With this technique, we can recover the original high-frequency information that was lost during image acquisition, which will help us to extract the necessary information that the image is intended to convey.

Examples of De-aliasing applications

De-aliasing has a plethora of applications in various fields. Here are a few examples:

Medical imaging:

De-aliasing is extensively used in medical imaging applications, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound imaging. The technique improves the quality of the acquired images and allows doctors to make more accurate and reliable diagnoses based on the extracted information.

Computer vision:

De-aliasing is also used in computer vision applications, such as object detection, recognition, and tracking. In-state-of-the-art object detection models, de-aliasing is used to pre-process the images for better detection and recognition of objects in the images.

Remote sensing:

In remote sensing, de-aliasing is used to enhance the image taken by satellite or airborne sensors. With de-aliasing, we can extract high-frequency information from the images, such as vegetation types, land cover, and soil moisture, which can be helpful in various agricultural and environmental studies.

Types of Aliasing

Before we move on to the de-aliasing technique, let's first understand the two types of aliasing that can occur while acquiring images.

Spatial aliasing:

Spatial aliasing is a phenomenon that occurs when the resolution of an image is too coarse to display signals that have a higher frequency than the image's Nyquist frequency. When spatial aliasing occurs, the image appears as if it contains lower frequency signals than it actually does, and the actual high-frequency information gets lost.

Temporal aliasing:

Temporal aliasing is a phenomenon that occurs when the sampling rate of the image is too slow to show the temporal frequencies of the phenomenon being recorded. Temporal aliasing appears as if it is a lower frequency phenomenon and results in the loss of information.

Techniques for De-aliasing:

Various techniques are used for de-aliasing, and each has its pros and cons. Here are a few popular techniques:

Wiener filtering:

Wiener filtering is a widely used technique for de-aliasing images. The technique uses a filter that removes the noise present in the image due to aliasing. The filter is designed on the basis of the estimation of noise and the signal's power spectral density. The technique works best when the noise is Gaussian, and the signal is stationary.

Maximum Entropy:

Maximum Entropy is another technique used for de-aliasing. In this technique, the image is reconstructed by finding the maximum entropy image that satisfies the constraints posed by the available data. The principle of maximum entropy is used to determine the distribution of the possible images that satisfy the constraints. The technique works best when there are few constraints and the data are noisy.

Laplacian Pyramid:

The Laplacian Pyramid technique for de-aliasing is based on the multi-resolution decomposition of an image. The method uses a series of low pass filters and high pass filters to decompose the image into various levels of resolution. After the decomposition, the high-frequency information is recovered using various interpolations techniques. The interpolated image is then reconstructed from the decomposed multi-resolution image to recover the original high-frequency information. The technique works best when the signal has a sparse representation in the space of the Laplacian Pyramid.

Non-Local Means:

Non-Local Means is another technique used for de-aliasing. In this technique, a pixel's value is estimated from the values of similar pixels present in the image. This technique works best when there are repetitive patterns in the image that can be exploited to determine the high-frequency information.

Conclusion:

De-aliasing is an essential technique to recover the original high-frequency information that was lost during image acquisition. With the advancements in technology, numerous techniques have been developed for de-aliasing, and each has its own set of pros and cons. De-aliasing is extensively used in various fields such as medical imaging, computer vision, remote sensing, and many more, where image quality plays a crucial role in decision making.

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