ABOUT DIGITAL PROCESSING OF ASTRONOMICAL IMAGES USING HISTOGRAM PROCESSING METHODS

Photos of extended objects are crucial for astronomers, as they contain enough detailed information about the celestial bodies that it is quite difficult to extract visually. Most of the information available for analyzing these objects begins with studying them with telescopes or satellites. Unfortunately, the quality of astronomical images is usually very poor compared to other real images, and this is due to the technical and physical features associated with the process of obtaining them. This increases the percentage of noise and makes it more difficult to directly use standard methods on the original image. Images taken from a satellite or telescope are almost always grayscale, but still contain some color information. However, an astronomical image can be obtained through a color filter. Different photodetectors also usually have different sensitivity to different colors (wavelengths). In our paper, we will present a method for processing astronomical images, using histogram processing, which can be successfully used to improve images, and post-processing.

Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration

Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.

Multiframe Astronomical Image Registration Based on Block Homography Estimation

Due to the influence of atmospheric turbulence, a time-variate video of an observed object by using the astronomical telescope drifts randomly with the passing of time. Thereafter, a series of images is obtained snapshotting from the video. In this paper, a method is proposed to improve the quality of astronomical images only through multiframe image registration and superimposition for the first time. In order to overcome the influence of anisoplanatism, a specific image registration algorithm based on multiple local homography transformations is proposed. Superimposing registered images can achieve an image with high definition. As a result, signal-to-noise ratio, contrast-to-noise ratio, and definition are improved significantly.