Relevance gradient descent for parameter optimization of image enhancement

Abstract

Machine learning plays a pivotal role in constructing learning models and leveraging abundant image data for feature extraction, which is essential for achieving accurate and efficient image enhancement through optimization algorithms. Traditional gradient descent methods are confined to optimizing independent parameters, while recent algorithms neglect the interdependence among parameters, resulting in prolonged iteration times for each method. To address this issue, we propose a novel Correlation-Based Gradient Descent (RGD) algorithm tailored for enhancing low-light images. RGD leverages inter-iteration results for mutual parameter transmission and optimization, employing convergence or oscillation criteria to select the final enhanced image and find the optimal parameter combination for image enhancement, thereby enhancing parameter optimization efficiency. Specifically, each iteration is divided into multiple operations, with each operation adopting different enhancement schemes. During each iteration, the results of different operation parameters are shared, which facilitates the discovery of optimal parameters for low-light image enhancement. Subsequently, corresponding enhancement operations are performed based on this parameter combination to optimize standard indicators. Our experiments on medical and natural low-light scenarios demonstrate that our RGD method achieves a significant reduction in iteration time, with at least a 17% decrease compared to exhaustive loop methods.