The invention discloses a
retina bionics-driven super-resolution reconstruction model and an implementation method thereof. The reconstruction model is composed of a shallow extraction and lateral suppression module (LIM) and a deep
feature extraction and reconstruction module (RM). The shallow layer extraction simulates the
processing process of photoreceptor cells in a biological vision
system on optical signals, and low-level feature information is extracted from a low-resolution image so as to realize primary
processing of image signals; the side inhibition module LIM simulates a horizontal
cell side inhibition mechanism to capture signals, and performs different intensity inhibition on features; the deep
feature extraction adopts multi-layer
feature fusion and a
layered structure, imitates multiple functions of bipolar cells, gradually extracts and fuses feature information of different
layers, and improves the naturalness of the whole image; the reconstruction module RM is composed of an up-sampling block and a
convolution, simulates
ganglion cells, and is responsible for generating a final high-resolution reconstruction image. According to the method, a satisfactory reconstruction result is obtained for an image in a complex scene, and meanwhile it is proved that a
retina biological system is feasible to be migrated to an image super-resolution reconstruction technology.