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 this https URL

立体图像超分辨率（SR）是指从双摄像头设备通常捕获的一对低分辨率（LR）图像重建出高分辨率（HR）图像。我们提出了一种名为 NAFRSSR 的简单而高效的立体图像 SR 模型，通过引入递归连接和轻量化构成模块来改进先前最先进的模型 NAFSSR。我们的 NAFRSSR 模型由非线性激活免费和基于组卷积的块（NAFGCBlocks）以及深度分离立体交叉注意力模块（DSSCAMs）组成。NAFRGCBlock 通过从 NAFBlock 中删除简单通道注意机制并使用组卷积来改进特征提取和减少参数数量。DSSCAM 通过将 SCAM 中的 1x1 逐点卷积替换为共享权重的 3x3 深度卷积来增强特征融合和减少参数数量。此外，我们还提出将可训练的边缘检测算子纳入到 NAFRSSR 中以进一步提高模型性能。我们设计了四个不同尺寸的 NAFRSSR 变体，即 NAFRSSR-Mobile、NAFRSSR-Tiny、NAFRSSR-Super 和 NAFRSSR-Base，它们都比先前最先进的模型具有更少的参数、更高的 PSNR/SSIM 和更快的速度。特别地，据我们所知，NAFRSSR-M 是最轻量级（0.28M 参数）和最快（50 毫秒推理时间）的模型，在基准数据集上获得了高达 24.657 dB/0.7622 的平均 PSNR/SSIM。代码和模型将发布在此 https URL