Image recovery is a critical challenge in various applications, such as medical imaging and remote sensing, where high-quality reconstruction from incomplete or noisy data is essential. Traditional optimization methods, including the Split Bregman algorithm (SBA), have been widely used due to their effectiveness in solving regularization-based problems. However, SBA often suffers from slow convergence, limiting its practical utility in time-sensitive scenarios. This paper proposes a parameter-adaptive heuristic to accelerate the convergence of SBA while maintaining reconstruction accuracy. The key idea is to dynamically adjust the algorithm's internal parameters based on local gradient information, reducing unnecessary iterations. Experiments on synthetic and real images demonstrate that the proposed method achieves convergence. PSNR quantitative metrics validate the improvements, while visual comparisons confirm the preservation of fine details. The adaptive heuristic is simple to implement and computationally efficient, making it suitable for large-scale imaging problems. This work contributes a practical improvement to SBA, broadening its applicability in real-time image processing tasks. Future extensions could explore integration with deep learning frameworks for further optimization.