Optimized RainDNet: an efficient image deraining method with enhanced perceptual quality.

RainDNet is an advanced image deraining model that refines the "Multi-Stage Progressive Image Restoration Network" (MPRNet) for superior computational efficiency and perceptual fidelity. RainDNet's innovative architecture employs depthwise separable convolutions instead of MPRNet's traditional ones, reducing model complexity and improving computational efficiency while preserving the feature extraction ability. RainDNet's performance is enhanced by a multi-objective loss function combining perceptual loss for visual quality and Structural Similarity Index Measure (SSIM) loss for structural integrity. Experimental evaluations demonstrate RainDNet's superior performance over MPRNet in terms of Peak Signal-to-Noise Ratio (PSNR), SSIM, and BRISQUE (Blind Referenceless Image Spatial Quality Evaluator) scores across multiple benchmark datasets, underscoring its aptitude for maintaining image fidelity while restoring structural and textural details. Our findings invite further explorations into more efficient architectures for image restoration tasks, contributing significantly to the field of computer vision. Ultimately, RainDNet lays the foundation for future, resource-efficient image restoration models capable of superior performance under diverse real-world scenarios. [ABSTRACT FROM AUTHOR]