This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) for fast and accurate single-image super-resolution. LapSRN progressively reconstructs sub-band residuals of high-resolution images using a cascade of convolutional neural networks (CNNs). At each pyramid level, the model takes coarse-resolution feature maps as input, predicts high-frequency residuals, and uses transposed convolutions for upsampling to finer levels. The method avoids bicubic interpolation, reducing computational complexity and improving reconstruction quality. LapSRN is trained with a robust Charbonnier loss function and generates multi-scale predictions in one feed-forward pass, enabling resource-aware applications. Extensive evaluations on benchmark datasets show that LapSRN outperforms state-of-the-art methods in terms of speed and accuracy. The network's progressive reconstruction allows it to handle different scaling factors and computational constraints, making it suitable for real-world applications such as enhancing video resolution. The LapSRN architecture is compared with existing methods, and results demonstrate its effectiveness in reducing artifacts and improving image quality. The model is also applied to real-world photos and videos, showing superior performance in reconstructing fine details and reducing ringing artifacts. The paper also discusses limitations, such as the inability to hallucinate details when the input image lacks sufficient structure. Overall, LapSRN provides a fast and accurate solution for single-image super-resolution, with potential applications in various domains requiring resource-efficient image enhancement.This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) for fast and accurate single-image super-resolution. LapSRN progressively reconstructs sub-band residuals of high-resolution images using a cascade of convolutional neural networks (CNNs). At each pyramid level, the model takes coarse-resolution feature maps as input, predicts high-frequency residuals, and uses transposed convolutions for upsampling to finer levels. The method avoids bicubic interpolation, reducing computational complexity and improving reconstruction quality. LapSRN is trained with a robust Charbonnier loss function and generates multi-scale predictions in one feed-forward pass, enabling resource-aware applications. Extensive evaluations on benchmark datasets show that LapSRN outperforms state-of-the-art methods in terms of speed and accuracy. The network's progressive reconstruction allows it to handle different scaling factors and computational constraints, making it suitable for real-world applications such as enhancing video resolution. The LapSRN architecture is compared with existing methods, and results demonstrate its effectiveness in reducing artifacts and improving image quality. The model is also applied to real-world photos and videos, showing superior performance in reconstructing fine details and reducing ringing artifacts. The paper also discusses limitations, such as the inability to hallucinate details when the input image lacks sufficient structure. Overall, LapSRN provides a fast and accurate solution for single-image super-resolution, with potential applications in various domains requiring resource-efficient image enhancement.