Abstract

The digital age thrives on image processing, a technology critical for healthcare and security. This paper proposes a robust approach to improve image quality and empower further analysis through innovative preprocessing techniques. Our approach attempts to implement image data systematically, ensuring it's ready for advanced processing. Standardization with Bicubic Interpolation: Input images are resized to a uniform dimension using Bicubic Interpolation. This ensures compatibility within datasets, regardless of their original sizes, while preserving the image's proportions. Separating Brightness for Sharper Analysis: Images are converted from RGB to YCbCr color space. This separates the image data into brightness (luma) and color (chrominance) components. Focusing on the bright information is crucial for noise reduction and edge detection. Enhanced Clarity with NoiseNixie Rejuvenation Filter: Our novel NoiseNixie Rejuvenation Filter (NNRF) tackles noise, a standard image quality hurdle. This filter incorporates noise variation and light correction adjustments, resulting in sharper and clearer images. Fast Fourier Transform for Refined Processing: The Fast Fourier Transform (FFT) converts image data into the frequency domain. This transformation unveils hidden patterns within the image and allows for precise adjustments. The data is then converted back using the Inverse FFT, preparing the image for in-depth analysis. By implementing these techniques, our preprocessing pipeline empowers researchers and practitioners to unlock valuable insights from image data. This comprehensive approach paves the way for advancements in image processing across various applications. From medical imaging to autonomous vehicles, high-quality image analysis is essential, and this method provides a robust foundation for achieving that goal.