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Recently, convolutional neural networks (CNNs) have facilitated the rapid development of image super-resolution. Most deep networks are challenging to apply to the real world due to their high cost of memory storage and computational complexity. This paper addresses this issue by proposing a lightweight subpixel sampling network (SSN). Specifically, we use a traditional encoder-decoder structure and replace the deconvolution and pooling layers by subpixel up-sampling and down-sampling without parameters. Subpixel sampling retains more image information than other sampling methods. In addition, we propose parsimonious spatial and channel attention blocks through which multi-scale features are fused and more image textures can be recovered. Through extensive experiments, we validate the effectiveness of subpixel sampling, spatial attention block, and channel attention block. In terms of quantitative metrics and visual quality, our models achieve performance comparable to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-exposure fusion (MEF) is widely researched and applied to high dynamic range (HDR) imaging, where one of the most challenging problems is the artifacts caused by the motion of objects between input images. Recently, deep learning methods have been widely applied for HDR imaging with excellent results, showing significant advantages. However, many methods cannot avoid artifacts due to inaccurate alignment before merging HDR images. In this paper, we propose an end-to-end network (C-ED-GMNET) with a channel and spatial attention network, an encoder–decoder network and a gradual merging network for generating artifact-free HDR images in dynamic scenes. The attention module consists of two submodules to identify useful features and exclude harmful components in inputs from both channel and spatial dimensions, respectively. The attention-guided feature maps are sent to the encoders for further feature extraction, and then, the outputs are sent to a gradual merging module consisting of two steps to generate deep features progressively. Besides, the differences between the merged image and the original images are identified by applying global residual learning with all the inputs, and the merged image feature is recovered by the decoder to obtain the final HDR image. Quantitative and qualitative experiments on two public datasets show that the proposed C-ED-GMNET produces better results than existing state-of-the-art methods and significantly reduces the runtime due to the encoders which reduce the amount of computation. [ABSTRACT FROM AUTHOR]