What Are Image Processing Algorithms?

The image algorithm refers to the algorithm used to process the image. Including image denoising, image transformation, image analysis, image compression, image enhancement, image blur processing, etc.

The image algorithm refers to the algorithm used to process the image. Including image denoising, image transformation, image analysis, image compression, image enhancement, image blur processing, etc.

Image algorithm

Image transformation includes stretching, shrinking, distorting, rotating, and Fourier transform of an image. The original image is generally referred to as a spatial domain image, and the transformed image is referred to as a conversion domain image. The converted domain image can be inversely transformed into a spatial domain image. The transformations used in image processing are unitary transformations, that is, transformations in which the transformation kernel satisfies orthogonal conditions. The image after unitary transformation is often more conducive to feature extraction, enhancement, compression, and image coding.

Image algorithm

Image compression refers to the technique of representing the original pixel matrix with lossless or lossless bits, and is also called image coding. The information age has brought about an "information explosion", which has greatly increased the amount of data. Therefore, it is necessary to effectively compress data regardless of transmission or storage. In remote sensing technology, various space probes use compression coding technology to send the huge information they get back to the ground. Image compression is the application of data compression technology to digital images. Its purpose is to reduce redundant information in image data and to store and transmit data in a more efficient format.

Image algorithm image enhancement

Image enhancement can be divided into two categories: frequency domain method and space domain method. The former regards the image as a two-dimensional signal, and performs signal enhancement based on the two-dimensional Fourier transform. Using low-pass filtering (that is, passing only low-frequency signals) can remove noise in the picture; using high-pass filtering can enhance high-frequency signals such as edges, and make blurred pictures clear. Representative algorithms in the latter spatial domain method include the local averaging method and the median filtering method (taking the intermediate pixel value in the local neighborhood). They can be used to remove or reduce noise.

The method of image enhancement is to add some information or transformation data to the original image by some means, selectively highlight the features of interest in the image or suppress (mask) some unwanted features in the image, so that the image matches the visual response characteristics . In the image enhancement process, the causes of image degradation are not analyzed, and the processed image may not necessarily approach the original image. Image enhancement technology can be divided into two categories based on spatial domain algorithms and frequency domain based algorithms based on the space in which the enhancement process is located. The algorithm based on the spatial domain directly performs operations on the gray level of the image during the processing. The algorithm based on the frequency domain is an indirect enhanced algorithm that modifies the transform coefficient value of the image within a certain transform domain of the image.

Space-based algorithms are divided into point arithmetic and neighborhood denoising algorithms. The point arithmetic algorithms include gray level correction, gray level transformation, and histogram correction. The purpose is to make the image uniform, or to expand the dynamic range of the image, and to expand the contrast. Neighborhood enhancement algorithms are divided into image smoothing and sharpening. Smoothing is generally used to eliminate image noise, but it also easily causes blurring of edges. Common algorithms include mean filtering and median filtering. The purpose of sharpening is to highlight the edge contour of the object, which is convenient for target recognition. Commonly used algorithms include gradient methods, operators, high-pass filtering, mask matching methods, and statistical difference methods. Spatial methods include histogram equalization, gray scale linear change, image smoothing, and image sharpening.

Frequency-domain methods include low-pass filtering and high-pass filtering. Low-pass filtering includes ideal low-pass filtering, Butterworth low-pass filtering, exponential low-pass filtering, trapezoidal low-pass filtering, and high-pass filtering includes ideal high-pass filtering, Butterworth high-pass filtering, exponential high-pass filtering, trapezoidal high-pass filtering, and color Image enhancement (true color, false color, pseudo color enhancement).

Image algorithm image blur processing

Image blur processing includes image blur processing and image denoising processing, image blur processing includes motion blur (Wiener filtering, least mean square filtering, blind convolution, etc.), Gaussian blur, etc. Image denoising processing includes Gaussian noise processing (Wiener Filtering, spline interpolation, low-pass filtering), salt and pepper noise processing, etc.

Image algorithm image interpolation

The traditional interpolation methods are: nearest neighbor interpolation, bilinear interpolation, bisquare interpolation, bicubic interpolation, and other higher-order methods. Nearest neighbor interpolation and bilinear interpolation algorithms are prone to aliasing, and the quality of the generated images is not good. Therefore, it is generally used only when the image quality is not high. Double-squared interpolation and double-cubic interpolation are essentially "low-pass filters" that lose a lot of high-frequency information while enhancing the smoothing effect of the image. And in many applications. The detail information is very important. Consider how to keep the details as much as possible while ensuring smooth effects. ^[1]