Image degradation caused by noise and blur remains a persistent challenge in imaging systems, stemming from limitations in both hardware and methodology. Single-image solutions face an inherent tradeoff between noise reduction and motion blur. While short exposures can capture clear motion, they suffer from noise amplification. Long exposures reduce noise but introduce blur. Learning-based single-image enhancers tend to be over-smooth due to the limited information. Multi-image solutions using burst mode avoid this tradeoff by capturing more spatial-temporal information but often struggle with misalignment from camera/scene motion. To address these limitations, we propose a physical-model-based image restoration approach leveraging a novel dual-exposure Quad-Bayer pattern sensor. By capturing pairs of short and long exposures at the same starting point but with varying durations, this method integrates complementary noise-blur information within a single image. We further introduce a Quad-Bayer synthesis method (B2QB) to simulate sensor data from Bayer patterns to facilitate training. Based on this dual-exposure sensor model, we design a hierarchical convolutional neural network called QRNet to recover high-quality RGB images. The network incorporates input enhancement blocks and multi-level feature extraction to improve restoration quality. Experiments demonstrate superior performance over state-of-the-art deblurring and denoising methods on both synthetic and real-world datasets. The code, model, and datasets are publicly available at https://github.com/zhaoyuzhi/QRNet.

Retrieving 3D shapes from 2D human sketches has received increasing attention in computer vision and computer graphics. Most previous methods projected 3D shapes from numerous viewpoints and then extracted features of 3D shapes from these projections and calculated the similarity with sketches. However, due to the unknown pose of 3D shapes, viewpoints were usually sampled uniformly from a sphere coordinate. Hence, some projections acquired insufficient descriptions of 3D shapes. In this paper, we proposed a view selection algorithm to find the most reasonable viewpoints, which can benefit representation learning for 3D shapes. Additionally, to indicate the apparent discrepancy between sketches and 3D shapes, we leveraged a generalized similarity model to encourage the accuracy of cross-domain feature matching. We first computed line renderings of 3D shapes from an enormous number of viewpoints. Then, we calculated the similarity of shapes between line renderings and sketches. In this vein, we obtained several superior projections. Second, we implemented a sketch network to extract features of the sketch and a shape network to extract features of projections. We combined the features of different projections to secure the compact representation for 3D shapes. Finally, a metric network was constructed using a cross-domain similarity model, and we trained the metric network with triplet loss. Online hard sample mining was leveraged to accelerate the convergence of the network. We evaluated our method on SHREC‘13 and SHREC’14 sketch track benchmark datasets. The experimental results demonstrated that both view selection and cross-domain similarity models were able to encourage retrieval performance.