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In this paper we propose a novel method for infrared and visible image fusion where we develop nest connectionbased network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-theart methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse.

Feature extraction and processing are key tasks in the Image fusion algorithm, while most of deep learning-based methods use deep features directly without feature processing. This leads to the fusion performance degradation in some cases. To solve this drawback, in this paper, a novel fusion framework based on deep features and zero-phase component analysis (ZCA) is proposed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and l 1 -norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed algorithm achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://github.com/hli1221/imagefusion_resnet50.

Seventy-seven children between the ages of 6 and 10 years, with severe mixed receptive-expressive specific language impairment (SLI), participated in a randomized controlled trial (RCT) of Fast ForWord (FFW; Scientific Learning Corporation, 1997, 2001). FFW is a computer-based intervention for treating SLI using acoustically enhanced speech stimuli. These stimuli are modified to exaggerate their time and intensity properties as part of an adaptive training process. All children who participated in the RCT maintained their regular speech and language therapy and school regime throughout the trial. Standardized measures of receptive and expressive language were used to assess performance at baseline and to measure outcome from treatment at 9 weeks and 6 months. Children were allocated to 1 of 3 groups. Group A (n = 23) received the FFW intervention as a home-based therapy for 6 weeks. Group B (n = 27) received commercially available computer-based activities designed to promote language as a control for computer games exposure. Group C (n = 27) received no additional study intervention. Each group made significant gains in language scores, but there was no additional effect for either computer intervention. Thus, the findings from this RCT do not support the efficacy of FFW as an intervention for children with severe mixed receptive-expressive SLI.

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