The portable X-ray imaging is essential in many fields, intrinsic problems with image quality frequently restrict its usefulness. The proposed work aims to improve the quality of portable X-ray images. Using sophisticated algorithms for image sharpening, contrast enhancement, and noise reduction results in notable gains in image clarity, contrast, and overall quality. However, by conducting a comprehensive comparative analysis of three different approaches—Canny edge detection alone, CLAHE in combination with Canny edge detection, and a novel combination of wavelet-based anisotropic diffusion, CLAHE, and Canny edge detection—this analysis goes beyond traditional methodologies. The later integration of CLAHE, Canny edge detection, and wavelet-based anisotropic diffusion extensive experimentation and analysis have shown that the CLAHE and Canny edge detection approach produces the most promising results, boosting the interpretability and diagnostic accuracy of portable X-ray pictures. These developments have wide-ranging effects on medical diagnostics, security checks, and industrial inspections. The proposed work aims to provide important insights for optimizing the accuracy and utility of portable X-ray imaging by elucidating theoretical concepts, methodological approaches, and recent advancements. Combining these techniques not only provides practitioners with more precise and targeted visualizations, but it also improves the quality of decision-making processes across a range of domains and applications. The conclusion highlights the enormous potential for image processing to enhance portable X-ray imaging and broaden its uses beyond medical diagnosis.