Abstract
Convolution neural networks facilitate the significant process of single image super‐resolution (SISR). However, most of the existing CNN‐based models suffer from numerous parameters and excessively deeper structures. Moreover, these models relying on in‐depth features commonly ignore the hints of low‐level features, resulting in poor performance. This paper demonstrates an intriguing network for SISR with cascading and residual connections (CASR), which alleviates these problems by extracting features in a small net called head module via the strategies based on the depthwise separable convolution and deformable convolution. Moreover, we also include a cascading residual block (CAS‐Block) for the upsampling process, which benefits the gradient propagation and feature learning while easing the model training. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method is superior to the latest SISR methods in terms of quantitative indicators and realistic visual effects.
Highlights
Superresolution (SR) image reconstruction is widely used in various applications, such as military surveillance, medical diagnostics [1, 2], remote sensing [3], and video streaming [4, 5]
We introduce the naturalness image quality evaluator (NIQE) [33] and Perceptual Index (PI) [22] to perform the evaluation
We set depthwise separable convolution operations in head module shown in Figure 2, which were first illustrated in the Inception net in the proposed model, and were able to reduce the size of the network parameters effectively
Summary
Superresolution (SR) image reconstruction is widely used in various applications, such as military surveillance, medical diagnostics [1, 2], remote sensing [3], and video streaming [4, 5]. Single image superresolution (SISR) is aimed at reconstructing a high-resolution (HR) image from its counterpart low-resolution (LR) input, which is an essential and classic task in computer vision. Physical constraints limit the conduction of high-resolution pictures. A series of successful works brought attention to the research community. The task of recovering HR images ISR from its counterpart (LR) version ILR is ill-posed. Researchers have made many efforts to this task and invented numerous algorithms, including interpolation-based, reconstruction-based, and learning-based methods [1], respectively
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