Benefiting from the powerful data learning and representation capabilities of neural networks, Learned Image Compression (LIC) methods have demonstrated better Rate-Distortion (RD) performance than traditional image compression frameworks. In this paper, we analyze the role of latent variables in image compression, both qualitatively and quantitatively. We then propose a latent variable compensation method to mitigate the loss introduced by quantization. We also introduce a regularization term for the latent variable mean square error into the loss function, providing more explicit guidance for the compression and reconstruction of the model’s internal latent variables. Additionally, we propose a noise compensation method that acts as a plug-and-play component to enhance reconstruction in lossy image compression without significant additional encoding or decoding time. We also present a data augmentation technique involving image inversion, which helps the training set conform to the symmetry inherent in probabilistic modeling for image compression tasks. Extensive experiments demonstrate that the proposed method enhances rate-distortion metrics and visual quality.

Current diffusion-based super-resolution methods for remote sensing images face two critical limitations: (1) excessive computational demands due to iterative sampling; and (2) semantic inconsistency in reconstructed images caused by stochastic denoising. To address these challenges, we propose SfmSR, a Semantics-guided Flow Matching model that achieves fast (1-5 steps) and accurate reconstruction through a novel two-stage framework. Stage 1 performs large-scale pre-training on diverse remote sensing data to learn robust multi-scale representations, while Stage 2 introduces semantic-guided fine-tuning, where extracted high-level features dynamically regulate the flow matching process. This dual-phase approach reduces sampling steps by two orders of magnitude while maintaining stability through deterministic ODE-based generation. Experimental results demonstrate that SfmSR outperforms existing methods on remote sensing image datasets such as Potsdam and Toronto. Moreover, SfmSR exhibits significant advantages in model complexity and inference efficiency, achieving fast inference with fewer parameters and lower memory usage, thus meeting real-time requirements in practical applications. Compared to state-of-the-art (SOTA) methods, SfmSR not only achieves superior reconstruction quality but also demonstrates clear advantages in inference speed and computational efficiency.