Lightweight CNN-Based Artifact Reduction for Scientific Error-bounded Lossy Compression

Lossy compression is widely used to reduce storage and transmission costs in large-scale scientific data, but it inevitably introduces artifacts that may compromise subsequent analysis. To address this issue, we propose a lightweight 3D convolutional architecture with a fixed-scale batch normalization strategy, ensuring stable training and fast inference. We further analyze the trade-offs related to network size and highlight an empirical relationship between the minimum achievable MSE loss and the corresponding training cost. We also validate the generalizability of the trained network. Experimental results on five representative scientific lossy compressors (SZ1, SZ2, SZP, SZX, and ZFP) and datasets from four diverse scientific domains demonstrate that our method consistently improves reconstruction quality: MSE is reduced by one to four orders of magnitude (10-10000×), corresponding to a PSNR gain of 10-40 dB, while keeping the inference time comparable to the compression runtime. A network trained on a single file generalizes well to other files within the same data set. These results highlight the potential of deep learning as an effective post-processing step to improve compressed scientific data.