Deep Learning Approaches for High Quality Image Restoration and Reconstruction

In this paper, we proposed a hybrid SRCNN + EfficientNet architecture for image denoising and reconstruction, combining SRCNN-style low-level convolutional filters with EfficientNet-based feature extraction. Evaluations on standard benchmarks (Kodak24, CBSD68, Urban100, and LIVE1) show consistent improvements in reconstruction quality, achieving average PSNR gains and favorable SSIM and MSE values compared to baseline methods. The results demonstrate that integrating lightweight convolutional layers with a modern backbone effectively captures both local textures and global semantics, improving perceptual fidelity and pixel-wise accuracy. Future work will explore incorporating perceptual and adversarial losses, multi-scale fusion strategies, and automated model scaling to further boost visual quality while preserving computational efficiency for deployment on resource-constrained devices..

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