# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 1 Introduction  
During the imaging process, atmospheric disturbances, camera aberrations, and relative motion can all cause blurred images and degrade the image quality. To eliminate the adverse effects of these factors, the current mainstream deblur algorithms mainly use the idea of deconvolution to restore a blurred image. The common single-image deblurred algorithms usually use the image information provided by the recovered image itself to estimate the blur kernel, and the prior information of this estimation algorithm mostly consists of the statistical results of general image features, such as intensity distribution, transform domain features, etc. [PERSON] et al. proposed a blind deblurring algorithm based on the statistical results of image gradients in 2006 [1], and [PERSON] et al. proposed a method based on sparse gradients in 2007 [2]. At present, similarly to single image blind deblur methods, the algorithm is based on the variational Dirichlet, proposed by [PERSON] et al. in 2015. The Xu method has a good restoration effect [3]. Most of the current methods for single image restoration that use blind deconvolution are based on the basic ideas of the above-mentioned two algorithms from [1; 2], which use the statistical features of the image's sparse characteristics in the intensity distribution for kernel estimation. The abovementioned blind deblur algorithms for single images have a better performance in deblurring (example: shape and detail) for cases where the features of the recovered image and the selection of the prior information are relatively close; for example, the image intensity distribution andthe statistical prior distribution are in the same heavy-tailed distributions [1]. However, the larger the difference between the distributions, the worse the effect of recovery is. Therefore, the method of obtaining more targeted image information, which would improve the deblur restoration effect, is the main research direction for blind deconvolution algorithms at present. With the advancement of technology, current imaging devices, such as smartphones and civilian drones, are usually equipped with multiple lenses to obtain images with different optical characteristics. When images of the same target are obtained from multiple lenses, there is more information derived from multiple-input channels than a single image provides. Researchers have explored optimizing the restoration of image deblurring using the prior side information provided by images [4; 5; 6; 7; 8; 9; 10; 11; 12]. In 2003, [PERSON] and [PERSON] et al. conducted deblur research using images with different exposure rates with time-series characteristics [4], using the clear area of a short exposure image to replace the blurred area in the same position of the long exposure, and finally obtained a clear image and improved the dynamic range, which is the theoretical origin of the commonly used HDR algorithm [5]. In 2005, [PERSON] et al. proposed using the spatial correlation of multi-frame blurred images to estimate the blur kernel [6]; [PERSON] et al. used long and short exposure images of the same objectives to perform a blind restoration of multi-frame images in 2007 [7]. The [PERSON] method had a higher calculation efficiency and better restoration. However, the imaging acquisition process is complicated, and images with different exposure parameters need to be obtained for the same target. There are higher requirements for imaging condition in this method. In 1993, the framework of the multi-channel-based degraded signal restoration algorithm was proposed by [PERSON] et al. [8]. In 2005, [PERSON] et al. proposed a multi-frame image restoration technology [9] that used the inherent correlation of multi-frame images as a priori information, which improved the restoration effect, and the method was optimized in 2007 [10] and 2012 [11].