text: Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the input sizes are large. In the segmentation network, apart from the pyramid backbone network, an extra decoder network is often employed to recover the spatial detail information. In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks. Specifically speaking, we use the residual features to learn the edges and details, and we also use the top-level feature to learn the coarse segmentation result. At the testing phase, the predicted residuals are used to enhance the details of the coarse segmentation result. Residual learning blocks split the network into several shallow sub-networks by level-wise training, which facilitates the gradient propagation in the RPNet. We then evaluate the proposed method and compare it with the recent state-of-the-art methods on CamVid and Cityscapes datasets. The proposed single-shot segmentation based on the RPNet achieves impressive results with high efficiency on the pixel-level segmentation task.
  @cite_0: Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution. However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results. In this paper, we propose the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution. The proposed network progressively reconstructs the sub-band residuals of high-resolution images at multiple pyramid levels. In contrast to existing methods that involve the bicubic interpolation for pre-processing (which results in large feature maps), the proposed method directly extracts features from the low-resolution input space and thereby entails low computational loads. We train the proposed network with deep supervision using the robust Charbonnier loss functions and achieve high-quality image reconstruction. Furthermore, we utilize the recursive layers to share parameters across as well as within pyramid levels, and thus drastically reduce the number of parameters. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of run-time and image quality.
  @cite_16: We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
summary: Super resolution can be considered as a process of details restoration, so learning sparse residuals is more efficient than learning full images itself of higher resolution. VDSR @cite_16 sets up a deep neural network to learn the high frequency details, which achieve great improvement on accuracy. LapSRN @cite_0 expands the conception to cascade residual learning blocks and progressively reconstructs the sub-band residuals of high-resolution images at multiple pyramid levels and further increase the performance.