The unprecedented surge of digital data in cloud storage systems presents a pressing predicament characterized by a substantial volume of redundant data, imposing a strain on storage infrastructure. In response, deduplication has emerged as a pivotal technique gaining prominence within large-scale storage systems. By excising redundant data, deduplication optimizes storage utilization and concurrently mitigates storage expenses. Given the pervasive use and dissemination of images as a prevalent form of multimedia content, their deduplication mandates specialized methods like exact image and near-exact deduplication. This study offers a comprehensive evaluation of extant techniques in image deduplication, encompassing diverse taxonomies pertinent to cloud data storage. It delves into the challenges and intricacies related to augmenting the efficacy of image deduplication concerning efficiency, computational demands, and system overload. Moreover, the paper scrutinizes existing image deduplication methodologies with the intent of addressing and surmounting these aforementioned challenges.