Grinding is a crucial manufacturing process at the final stage of the machining chain, involving removing material from the surface of machined parts using an abrasive grinding wheel. One of the primary challenges in this process is determining the appropriate time for dressing the grinding wheel, which is essential to restore its cutting efficiency. The previous study entitled "In-Dressing Acoustic Map by Low-Cost Piezoelectric Transducer" introduced an innovative technique for diagnosing the surface integrity of the grinding wheel, employing a methodology for generating acoustic images from an acoustic emission sensor and a piezoelectric diaphragm. However, acquiring sharp acoustic maps remained challenging due to intense noise and interferences typical of harsh industrial environments. The present work further investigates this issue by applying digital image processing techniques to enhance the acoustic maps, utilizing tools including Google Colab and libraries like OpenCV, NumPy, and Matplotlib.pyplot. These techniques include smoothing, equalization, and edge filtering, using methods such as Sobel, Canny, and Prewitt. Examination of the treated acoustic maps revealed more detailed and relevant features, allowing a more accurate assessment of dressing conditions. The results demonstrate the efficacy of digital image processing techniques in improving the evaluation of the grinding wheel's cutting condition, contributing significantly to the efficient management of dressing cycles. This improvement can be applied to other machining processes, such as drilling and milling, and integrated into IoT (Internet of Things) sensor systems for applications in Industry 4.0.