Infrared imaging technology plays an indispensable role in critical applications such as military surveillance, autonomous driving, and medical diagnostics. However, its inherent low-resolution and low-contrast characteristics often limit operational performance. While deep learning-based super-resolution (SR) techniques offer a software-driven solution, models face severe feature redundancy caused by simply stacking deep layers, and a lack of discriminative power in distinguishing critical textures from thermal noise. To address these issues, we proposed a novel Convolutional and Attention-based Super-Resolution Network (CASRNet). The novelty of our model lies in the synergistic fusion of a channel splitting (CS) strategy and a dual attention mechanism. First, the CS strategy decomposes feature maps into parallel streams, extracting diverse and less redundant representations. Second, a novel channel and spatial attention residual block (CSA_ResBlock) was designed to adaptively focus on informative feature channels and critical spatial boundaries. Quantitatively, CASRNet achieved superior performance on public benchmarks. Specifically, for the FLIR dataset (× 2), our model achieved a peak signal-to-noise ratio (PSNR) of 39.73 dB and structural similarity (SSIM) of 0.9639, outperforming the state-of-the-art infrared-specific model TherISuRNet by 0.48 dB and standard models like VDSR by 0.15 dB. Similar robust improvements (e.g., an exceptional 40.45 dB PSNR on the ThermalTau2 dataset) demonstrated the general applicability and high fidelity of CASRNet for real-world infrared enhancement tasks.