A+ is an improved version of Anchored Neighborhood Regression (ANR) for fast single-image super-resolution. It combines the strengths of ANR and Simple Functions (SF) to achieve better quality and efficiency. A+ uses the full training data instead of the dictionary for regression, leading to improved performance. The method is validated on standard images and compared with state-of-the-art methods, achieving a PSNR improvement of 0.2-0.7 dB over ANR and excellent time complexity, making it the most efficient dictionary-based super-resolution method. A+ maintains the time complexity of ANR while significantly improving quality. It uses a sparse dictionary of patches and learns sparse representations to map LR patches to HR patches. A+ outperforms other methods in both quality and speed, with a PSNR of 34.74 dB on Set14 and 35.54 dB on Set14 for ×3 upscaling. It is also significantly faster than other methods, with a running time of 0.23 seconds on average. A+ is efficient, with a time complexity of ANR and uses fewer anchor points for better performance. The method is effective for various applications and is suitable for real-time processing.A+ is an improved version of Anchored Neighborhood Regression (ANR) for fast single-image super-resolution. It combines the strengths of ANR and Simple Functions (SF) to achieve better quality and efficiency. A+ uses the full training data instead of the dictionary for regression, leading to improved performance. The method is validated on standard images and compared with state-of-the-art methods, achieving a PSNR improvement of 0.2-0.7 dB over ANR and excellent time complexity, making it the most efficient dictionary-based super-resolution method. A+ maintains the time complexity of ANR while significantly improving quality. It uses a sparse dictionary of patches and learns sparse representations to map LR patches to HR patches. A+ outperforms other methods in both quality and speed, with a PSNR of 34.74 dB on Set14 and 35.54 dB on Set14 for ×3 upscaling. It is also significantly faster than other methods, with a running time of 0.23 seconds on average. A+ is efficient, with a time complexity of ANR and uses fewer anchor points for better performance. The method is effective for various applications and is suitable for real-time processing.