Traditional lightweight image super-resolution reconstruction methods typically rely on single-scale convolutions to extract image features, simply aggregating shallow and deep features for image reconstruction. However, this approach overlooks the richness of receptive field information and the crucial role of intermediate latent features in image reconstruction, leading to limited interaction between convolutional kernels and consequently resulting in the loss of image detail information and low reconstruction accuracy. In light of this, this paper proposes a lightweight image super-resolution reconstruction method based on progressive receptive fields. The core of this method lies in the design of a dual-path ladder convolutional chain, which effectively integrates the overall structural information and local detailed features of images by gradually adjusting the size of the receptive field, thus realizing the diversified expression of information. Furthermore, a method for the fusion of multi-dimensional latent features is explored, aiming to fully exploit and utilize the correlations between multi-dimensional features. Experimental results demonstrate that compared to currently popular reconstruction methods, the proposed method excels in capturing image details.