With the increasing demands for processing images and videos at edge terminals, complementary metal oxide semiconductor (CMOS) hardware systems based on conventional Von Neumann architectures are facing challenges in terms of energy consumption, speed, and footprint. Neuromorphic devices, including resistive random access memory with integrated storage-computation characteristic and optoelectronic resistive random access memory with highly integrated in-sensor computing characteristic, show great potential applications in image processing due to their high similarity to biological neural systems and advantages of high energy efficiency, high integration level, and wide bandwidth. These devices can be used not only to accelerate large numbers of computational tasks in conventional image preprocessing and higher-level image processing algorithms, but also to implement highly efficient biomimetic image processing algorithms. In this paper, we first introduce the state-of-the-art neuromorphic resistive random access memory and optoelectronic neuromorphic resistive random access memory, then review the hardware implementation of and challenges to image processing based on these devices, and finally provide perspectives of their future developments.