Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).

Automobile sales analysis provides an important basis for production planning, procurement, supply-chain coordination, inventory management, and dealership resource allocation. However, dealership sales are jointly influenced by operational practices, market conditions, and regional policies. Conventional machine-learning models may have difficulty capturing spatial heterogeneity, maintaining predictive performance under regional sample imbalance, and providing interpretable evidence for managerial analysis. To address these issues, this study constructs a multi-source automotive dealership dataset based on automaker marketing records and proposes CDMEP, a multi-paradigm framework integrating spatial diagnosis, ensemble transfer learning, predictive interpretation, and assumption-conditional structural diagnosis. The framework comprises a Spatial Heterogeneity Decoupling Diagnostic Model (SHDM) for examining spatial clustering and regional differences; a Regional Adaptive Stacked Learner (RASL) combining balanced cross-regional transfer, TPE-tuned base learners, and constrained ensemble weighting; and a Causally Scrutable Optimization Policy (CSOP) incorporating SHAP-, GAM-, and PDP-based predictive interpretation, temporally ordered LiNGAM diagnosis, edge-level stability and sensitivity analysis. The results indicate the following: (1) dealership sales exhibit spatial clustering and regional heterogeneity in variables related to lead generation, customer conversion, and dealership service capability; (2) RASL achieves an R² of 0.875 on the independent test set, corresponding to an absolute improvement of 0.051 over the second-best benchmark in the present dataset; and (3) under the specified preprocessing procedures and LiNGAM assumptions, lead volume, customer traffic, and conversion rate repeatedly appear in candidate directional structures associated with next-month sales, although their estimated coefficients and diagnostic stability vary across regions. These findings provide preliminary evidence that CDMEP may serve as a useful analytical framework for regionally differentiated sales prediction and assumption-aware dealership analysis.