Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion

TL;DR

This paper introduces GradProm, a training strategy for task-driven medical image quality enhancement that aligns enhancement with recognition tasks, improving image quality without biasing the recognition model.

Contribution

The paper proposes a novel GradProm training method that selectively updates the enhancement model based on gradient alignment, addressing conflicting task requirements in medical image IQE.

Findings

GradProm outperforms existing methods on four medical datasets.

Theoretical proof ensures unbiased optimization of the enhancement model.

Experimental results demonstrate improved image quality and recognition accuracy.

Abstract

Thanks to the recent achievements in task-driven image quality enhancement (IQE) models like ESTR, the image enhancement model and the visual recognition model can mutually enhance each other's quantitation while producing high-quality processed images that are perceivable by our human vision systems. However, existing task-driven IQE models tend to overlook an underlying fact -- different levels of vision tasks have varying and sometimes conflicting requirements of image features. To address this problem, this paper proposes a generalized gradient promotion (GradProm) training strategy for task-driven IQE of medical images. Specifically, we partition a task-driven IQE system into two sub-models, i.e., a mainstream model for image enhancement and an auxiliary model for visual recognition. During training, GradProm updates only parameters of the image enhancement model using gradients of the visual recognition model and the image enhancement model, but only when gradients of these two sub-models are aligned in the same direction, which is measured by their cosine similarity. In case gradients of these two sub-models are not in the same direction, GradProm only uses the gradient of the image enhancement model to update its parameters. Theoretically, we have proved that the optimization direction of the image enhancement model will not be biased by the auxiliary visual recognition model under the implementation of GradProm. Empirically, extensive experimental results on four public yet challenging medical image datasets demonstrated the superior performance of GradProm over existing state-of-the-art methods.