How to realize image intensification

2021-08-31

Image intensification, the basis of night vision, is a complex conversion of energy particles that occurs within a vacuum tube. An image-intensifier system works by collecting photons through an objective lens, converting them to electrons via a photocathode, increasing the electrical energy with a microchannel plate (MCP), converting the electrical energy back to light using a phosphor screen and presenting the image for viewing through an eyepiece lens.

Image intensifier can be divided into two categories: frequency domain method and space domain method.

The method of image enhancement is to add some information or transform data to the original image by certain 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 .

The spatial method is to operate on the pixels in the image, which is described by the formula as follows:

g(x,y)=f(x,y)*h(x,y)

Among them, f(x,y) is the original image; h(x,y) is the space conversion function; g(x,y) represents the processed image.

The spatial domain-based algorithm directly calculates the gray level of the image. The frequency domain-based algorithm corrects the transformation coefficient value of the image in a certain transformation domain of the image. It is an indirect enhancement algorithm.

Algorithms based on spatial domain are divided into point operation algorithm [1] and neighborhood denoising algorithm [2].

The point calculation algorithm is gray level correction, gray level transformation and histogram correction, etc. The purpose is to make the image uniform, or expand the dynamic range of the image, and expand the contrast. Neighborhood enhancement algorithms are divided into two types: image smoothing and sharpening.

Smoothing is generally used to eliminate image noise, but it can also easily cause blurred edges. Commonly used algorithms include mean filtering and median filtering. The purpose of sharpening is to highlight the edge contours of objects to facilitate target recognition. Commonly used algorithms include gradient method, operator, high-pass filtering, mask matching method, statistical difference method and so on.