Image enhancement joint optimization method and system based on downstream task performance

By acquiring training data and task evaluation data, determining the target training mode and setting parameter update strategies, the stability and robustness issues between the image enhancement model and the downstream task model are resolved, achieving robust optimization of the image processing process and ensuring effective support of enhanced images for tasks in multiple scenarios.

CN121707879BActive Publication Date: 2026-05-15XINGHAN SPACE TIME (SHENZHEN) AEROSPACE INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610211788.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-13
Publication Date
2026-05-15
Estimated Expiration
2046-02-13

AI Technical Summary

Technical Problem

The lack of a stable task-driven optimization mechanism between the image enhancement model and the downstream task model leads to low robustness and stability in the image processing process. The enhancement training objective is disconnected from the task objective, making it difficult to form a reproducible and comparable task-driven evaluation benchmark, which affects the performance of downstream tasks.

Method used

By acquiring training datasets and task evaluation data, the target training mode is determined, and a parameter update strategy is set based on the target training mode. Alternating joint training or individual training is adopted, and the parameters of the image enhancement model and the task model are optimized by combining task metric information, thus forming an evaluation and closed-loop optimization mechanism based on downstream task performance.

Benefits of technology

It improves the robustness and stability of the image processing process, ensuring that the enhanced images have more robust task-side performance under multiple degradation and scene conditions. The selection of enhancement links is aligned with the performance goals of the task side, and a stable task-driven optimization mechanism is provided.

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Abstract

The embodiment of the application provides an image enhancement joint optimization method and system based on downstream task performance, and relates to the technical field of image processing. The method first acquires a training data set and task evaluation data, inputs sample images in the training data set into an image enhancement model to obtain enhanced images, and inputs the enhanced images into a task model to obtain task output results. Then, a target training mode is determined according to the task evaluation data, and a parameter update strategy is set based on the target training mode. Then, according to the parameter update strategy, task metric information is calculated according to the task output results and task annotation information, and the model parameters of the trained model are updated according to the task metric information. The method can form an evaluation and closed-loop optimization mode based on the performance of the downstream task as the core constraint, and by introducing the task metric information under controllable conditions, a task-driven optimization strategy and a stabilization mechanism are used in the training stage, so that the robustness and stability of the image processing process are improved.
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