In this paper, we present a uniform mathematical framework based on a robust kernel-based regression for the task of simultaneous single-image super-resolution and denoising. The given model is formulated as a convex 1 sparse optimization problem, which can be efficiently solved by the alternating direction method of multipliers (ADMM). Especially, the proposed method is applied to image patches to reduce computational time. Additionally, an iterative strategy is also incorporated into the approach to refine more image details. The extensive experiments on simulated natural images with additional sparse noise and real time-of-flight (ToF) images demonstrate the ability of simultaneously removing sparse noise and enhancing image resolution.INDEX TERMS Kernel-based regression, uniform mathematical framework, joint image super-resolution and denoising, ToF images.

Background An effective identification model is crucial to realise the real-time monitoring and early warning of forest fires from surveillance cameras. However, existing models are prone to generate numerous false alarms under the interference of artificial smoke such as industrial smoke and villager cooking smoke, therefore a superior identification model is urgently needed. Aims In this study, we tested the Transformer-based model FireFormer to predict the risk probability of forest fire from the surveillance images. Methods FireFormer uses a shifted window self-attention module to extract similarities of divided patches in the image. The similarity in characteristics indicated the probability of forest fires. The GradCAM algorithm was then applied to analyse the interest area of FireFormer model and visualise the contribution of different image patches by calculating gradient reversely. To verify our model, the monitoring data from the high-point camera in Nandan Mountain, Foshan City, was collected and further constructed as a forest fire alarm dataset. Key results Our results showed that FireFormer achieved a competitive performance (OA: 82.21%, Recall: 86.635% and F1-score: 74.68%). Conclusions FireFormer proves to be superior to traditional methods. Implications FireFormer provides an efficient way to reduce false alarms and avoid heavy manual re-checking work.

In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high-resolution output obtained by a previously proposed super-resolution network. Based on this observation, we design a simple but effective deep convolutional neural network to train the mapping between the image residuals and the corresponding ground-truth residuals. Furthermore, we combine the high-resolution output generated by the previous super-resolution network and the high-resolution residual output by the proposed neural network to yield the final high-resolution image. Extensive experiments on simulated natural images and real time-of-flight (ToF) images demonstrate the effectiveness of the proposed method from the aspects of visual and quantitative performance.