Super-resolution (SR) aims to restore a high-resolution (HR) image from its low-resolution (LR) counterpart. Existing works try to achieve an overall average recovery over all regions to provide better visual quality for human viewing. If we desire to explore the potential that performs super-resolution for machine recognition instead of human viewing, the solution should change accordingly. From this insight, we propose a new SR pipeline, called InstanceSR, which treats each region in the LR image differentially and consumes more resources to focus on the recovery of the foreground region where the instances exist. In particular, InstanceSR consists of an encoder that formulates the LR image into a set of various difficulty tokens according to the instances distribution in each sub-region, and a decoder based on a multi-exit network structure to recover the sub-regions corresponding to various difficulty tokens by consuming different computational resources. Experimental results demonstrate the superiority of the proposed InstanceSR over state-of-the-art models, especially the recovery of regions where instances exist, by extensive quantitative and qualitative evaluations on three widely used benchmarks containing small instances. Besides, the comparisons using SR results on three challenging small object detection benchmarks verify that our InstanceSR can consistently boost the detection accuracy and has great potential for subsequent machine recognition.

Eliminating image blur produced by various kinds ofmotion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing residual from blurry observation in feature space. These practices not only prevent the capture of spatially variable motion in the real world but also ignore the tai-lored handling of various motions in image space. In this paper, we propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative (MISC) Filter. In particular, we use a motion estimation net-work to capture motion information from neighborhoods, thereby adaptively estimating spatially-variant motion flow, mask, kernels, weights, and offsets to obtain the MISC Fil-ter. The MISC Filter first aligns the motion-induced blur-ring patterns to the motion middle along the predicted flow direction, and then collaboratively filters the aligned image through the predicted kernels, weights, and offsets to generate the output. This design can handle more general-ized and complex motion in a spatially differentiated man-ner. Furthermore, we analyze the relationships between the motion estimation network and the residual reconstruction network. Extensive experiments on four widely used bench-marks demonstrate that our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance. Code is available at https://github.com/ChengxuLiu/MISCFilter.

Detecting objects in large-scale drone-view images is notoriously challenging due to their uneven distribution and scale variation caused by photoing angles. Common approaches promote drone-view object detection by two-step detection (i.e., detecting sub-regions first) and multi-scale input. However, all these methods suffer from onerous computational costs since the high model complexity and input resolution. In this paper, we propose a novel one-step detector, calledSDPDet, to enable effective object learning in drone-view images. In particular, a Scale-separated Activation Pyramid (SAP) serves to focus on the regions with objects aggregated at each scale, and a Scale-separated Learnable Proposals (SLP) mechanism learns proposal boxes and corresponding features on these regions. By such design, the quantity of learnable proposals allows dynamic adjustment at each scale separately, which facilitates the objects learning of various distributions and scales with less computational costs. Experiments demonstrate SDPDet can significantly outperform the state-of-the-art one-step detectors on three widely-used benchmarks. On the most challenging VisDrone dataset, SDPDet with ResNet50 gains5.4%AP and6.9%AP$_{s}$improvements while running1.9×faster than previous models.