In low-level vision tasks, achieving harmony between visual quality and recognition accuracy is often challenging, as the two do not always align. Many existing approaches focus on optimizing downstream tasks by linking image quality to machine perception, typically incurring additional burdens such as extensive annotations and joint training. In this work, we demonstrate that independent low-level reconstruction algorithms can simultaneously enhance imaging quality and downstream perception accuracy. By conducting a comprehensive frequency-domain analysis, we identify high-frequency components as critical for both visual and perceptual tasks. To counteract the frequency information loss typically seen in ISP pipelines, we propose a Multi-Frequency Fusion Block (MFFB) for on-the-fly upsampling, alongside a Frequency-Aware Supervision (FAS) mechanism guided by discrete wavelet transform. Our method achieves a notable +0.32 dB improvement in smart ISP performance on the Zurich dataset. Moreover, without relying on assistance from downstream tasks, our approach demonstrates significant improvements in object detection and instance segmentation.

Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.

Consumer-grade cameras capture the RAW physical description of a scene and then process the image signals to obtain high-quality RGB images that are faithful to human visual perception. Conventionally, dense prediction scenes require high-precision recognition of objects in RGB images. However, predicting RGB data to exhibit the expected adaptability and robustness in harsh environments can be challenging. By capitalizing on the broader color gamut and higher bit depth offered by RAW data, in this paper, we demonstrate that RAW data can significantly improve the accuracy and robustness of object detectors in harsh environments. Firstly, we propose a general Pipeline for RAW Detection (PRD), along with a preprocessing strategy tailored to RAW data. Secondly, we design the RAW Corruption Benchmark (RCB) to address the dearth of benchmarks that reflect realistic scenarios in harsh environments. Thirdly, we demonstrate the significant improvement of RAW images in object detection for low-light and corrupt scenes. Specifically, our experiments indicate that PRD (using FCOS) outperforms RGB detection by 13.9mAP on LOD-Snow without generating restored images. Finally, we introduce a new nonlinear method called Functional Regularization (FR), which can effectively mine the unique characteristics of RAW data. The code is available at https://github.com/DreamerCCC/RawMining.

Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with limited pruning patterns and non-zero sequences with equal length, PCNN can be efficiently employed in hardware. Evaluated on VGG-16 and ResNet-18, our PCNN achieves the compression rate up to 8.4× with only 0.2% accuracy loss. We also implement a pattern-aware architecture in 55nm process, achieving up to 9.0× speedup and 28.39 TOPS/W efficiency with only 3.1% on-chip memory overhead of indices.