  Stereo image super-resolution (SR) refers to the reconstruction of a
high-resolution (HR) image from a pair of low-resolution (LR) images as
typically captured by a dual-camera device. To enhance the quality of SR
images, most previous studies focused on increasing the number and size of
feature maps and introducing complex and computationally intensive structures,
resulting in models with high computational complexity. Here, we propose a
simple yet efficient stereo image SR model called NAFRSSR, which is modified
from the previous state-of-the-art model NAFSSR by introducing recursive
connections and lightweighting the constituent modules. Our NAFRSSR model is
composed of nonlinear activation free and group convolution-based blocks
(NAFGCBlocks) and depth-separated stereo cross attention modules (DSSCAMs). The
NAFGCBlock improves feature extraction and reduces number of parameters by
removing the simple channel attention mechanism from NAFBlock and using group
convolution. The DSSCAM enhances feature fusion and reduces number of
parameters by replacing 1x1 pointwise convolution in SCAM with weight-shared
3x3 depthwise convolution. Besides, we propose to incorporate trainable edge
detection operator into NAFRSSR to further improve the model performance. Four
variants of NAFRSSR with different sizes, namely, NAFRSSR-Mobile (NAFRSSR-M),
NAFRSSR-Tiny (NAFRSSR-T), NAFRSSR-Super (NAFRSSR-S) and NAFRSSR-Base
(NAFRSSR-B) are designed, and they all exhibit fewer parameters, higher
PSNR/SSIM, and faster speed than the previous state-of-the-art models. In
particular, to the best of our knowledge, NAFRSSR-M is the lightest (0.28M
parameters) and fastest (50 ms inference time) model achieving an average
PSNR/SSIM as high as 24.657 dB/0.7622 on the benchmark datasets. Codes and
models will be released at https://github.com/JNUChenYiHong/NAFRSSR.
