  Image processing is a fundamental task in computer vision, which aims at
enhancing image quality and extracting essential features for subsequent vision
applications. Traditionally, task-specific models are developed for individual
tasks and designing such models requires distinct expertise. Building upon the
success of large language models (LLMs) in natural language processing (NLP),
there is a similar trend in computer vision, which focuses on developing
large-scale models through pretraining and in-context learning. This paradigm
shift reduces the reliance on task-specific models, yielding a powerful unified
model to deal with various tasks. However, these advances have predominantly
concentrated on high-level vision tasks, with less attention paid to low-level
vision tasks. To address this issue, we propose a universal model for general
image processing that covers image restoration, image enhancement, image
feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies
these diverse image processing tasks within a universal framework. Inspired by
NLP question answering (QA) techniques, we employ a visual prompting question
answering paradigm. Specifically, we treat the input-output image pair as a
structured question-answer sentence, thereby reprogramming the image processing
task as a prompting QA problem. PromptGIP can undertake diverse cross-domain
tasks using provided visual prompts, eliminating the need for task-specific
finetuning. Our methodology offers a universal and adaptive solution to general
image processing. While PromptGIP has demonstrated a certain degree of
out-of-domain task generalization capability, further research is expected to
fully explore its more powerful emergent generalization.
