The raw depth images captured by RGB-D cameras using Time-of-Flight (TOF) or structured light often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts, which limit their applications in downstream vision tasks. Existing methods address this problem through depth completion in the image domain, but they overlook the physical characteristics of raw depth images. It has been observed that the presence of invalid depth areas alters the frequency distribution pattern. In this work, we propose a Spatio-Spectral Mutual Learning framework (S2ML) to harmonize the advantages of both spatial and frequency domains for depth completion. Specifically, we consider the distinct properties of amplitude and phase spectra and devise a dedicated spectral fusion module. Meanwhile, the local and global correlations between spatial-domain and frequency-domain features are calculated in a unified embedding space. The gradual mutual representation and refinement encourage the network to fully explore complementary physical characteristics and priors for more accurate depth completion. Extensive experiments demonstrate the effectiveness of our proposed S2ML method, outperforming the state-of-the-art method CFormer by 0.828 dB and 0.834 dB on the NYU-Depth V2 and SUN RGB-D datasets, respectively.

Despite well-documented consequences of the U.S. government's 1930s housing policies on racial wealth disparities, scholars have struggled to quantify its precise financial effects due to the inaccessibility of historical property appraisal records. Many counties still store these records in physical formats, making large-scale quantitative analysis difficult. We present an approach scholars can use to digitize historical housing assessment data, applying it to build and release a dataset for one county. Starting from publicly available scanned documents, we manually annotated property cards for over 12,000 properties to train and validate our methods. We use OCR to label data for an additional 50,000 properties, based on our two-stage approach combining classical computer vision techniques with deep learning-based OCR. For cases where OCR cannot be applied, such as when scanned documents are not available, we show how a regression model based on building feature data can estimate the historical values, and test the generalizability of this model to other counties. With these cost-effective tools, scholars, community activists, and policy makers can better analyze and understand the historical impacts of redlining.

The single-image deraining aims to restore clean scenes from rainy inputs by eliminating precipitation artifacts. Current methods often neglect the directional nature of rain streaks-a critical oversight that causes heterogeneous degradation, particularly in texture regions aligned with rain orientations. To address this issue and advance image deraining, we propose a novel direction-aware attention wavelet network (DAWN) for rain streaks removal. DAWN has several key distinctions and innovative features compared with existing wavelet transform-based methods: 1) introducing vector decomposition to parameterize rain distribution through vertical (V) and horizontal (H) component decomposition, enabling explicit direction-aware representation; 2) devising a novel direction-aware attention module (DAM) to learn projection/transformation parameters via coordinate attention mechanisms for precise rain removal and texture preservation; and 3) exploring practical composite constraints to jointly optimize structural coherence, detail fidelity, and chrominance accuracy. Building upon the conference version (DAWN), we devise DAWN+ with enhanced capabilities: 1) decoupling diagonal coefficient learning to eliminate frequency aliasing by characterizing diagonal components with dedicated projection parameters; 2) dividing vector decomposition and parameter fitting into multiple stages to reduce error accumulation; and 3) applying cross-frequency mutual representation to boost training and performance. Experiments across six tasks (deraining, raindrop/rainhaze removal, dehazing, and low-light/underwater enhancement) demonstrate the portability and reusability of these strategies. Meanwhile, DAWN+ delivers significant performance gains over DAWN, achieving an average peak signal to noise ratio (PSNR) increase of 1.17 dB with an acceptable complexity increase. Meanwhile, DAWN+ achieves the competitive performance to the state-of-the-art DRSformer (gaining 0.15 dB in PSNR) while saving 94.4% and 95% model parameters and inference time, respectively.