To achieve effective recovery of degraded images from harsh battlefield environments and reduce the interference of environmental factors on battlefield situational awareness, a new and end-to-end image recovery method, gated sampling network(GSNet), is constructed. The network adopts encoding block-decoding block as the basic architecture, CNNs and gated convolution as the encoding and decoding mechanism, compression and excitation network as the connection mechanism between encoding and decoding blocks, rescaling of higher-order information importance to distinguish targets and background features, and the channel granularity factor compression method as the light-weighting strategy to achieve rapid recovery of battlefield degraded environment images. The relevant experimental results show that the GSNet model can achieve a PSNR of 19.35 dB and an SSIM of 0.724, which are better than the compared mainstream image recovery algorithms in both objective metrics evaluation and subjective visual performance. The lightweight GSNet model reduces the number of parameters, FLOPs, and single image processing time by 56.6%, 54.6%, and 55.56%, respectively, with smaller improvements in PSNR and SSIM.