An Improved BTV-Based Image Deblurring Algorithm Based on a Low-Resolution Image Constraint

Multi-frame image super-resolution reconstruction (SRR) algorithms are typically divided into two steps, data fusion and image deblurring, in order to reduce computational complexity. However, some small or weak detail signals lost in the data fusion step cannot be recovered by the conventional image deblurring method. Therefore, a low resolution image constraint (LRIC) was introduced into the traditional deblurring optimization based on the bilateral total variation (BTV) regularization, and then a new deblurring method named BTV-LRIC was obtained in the LRIC based deblurring optimization using gradient descent method. The experiments show that, for data fusion images with different image contents or obtained using different data fusion methods, BTV-LRIC is superior to the TV and BTV method in terms of both visual perception and objective scores.