Розробка та застосування комп’ютерної програми для оцінки якості обробки зображень на основі дослідження згорток

• Date: 2025
• Main Authors: Царенко, М.О., Партека, А.Р., Лавров, М.В., Білинський, Й.Й.
• Format: Article
• Language: Ukrainian
• Published: Vinnytsia National Technical University 2025
• Subjects: обробка зображень; згортка; ядро згортки; просторова фільтрація; комп'ютерний зір; цифрова обробка сигналів; пікове співвідношення сигналів до шуму; PSNR; середньоквадратична помилка; MSE; метрики оцінки якості зображень; фільтр розмиття; фільтр загострення; фільтр тиснення; виявлення контурів; гаусівський шум; імпульсний шум; шум «сіль-перець»; Java; AWT; Swing
• Online Access: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/783
• Journal Title: Optoelectronic Information-Power Technologies

 Cluster-based digital filters occupy a key place in computer image processing programs for adjusting the shift in sharpness, the visible border, and so on. Using the method of learning the power of such filters, you know, the beginners and the students have developed a computer program that makes it possible to scientifically, vikorista kernels of different sizes to isolate the differences in the Gortkov filters (sharpness shift, pitch, edge detection, embossing) to process the image, as well as evaluate the brightness of their work using additional criteria of peak signal to noise ratio (PSNR) compared to the original and edited images. The program is implemented in object-oriented Java programming with the AWT and Swing libraries, which are designed for processing filters of any size in JPG, JPEG, PNG, BMP or GIF formats. The principles of operation of the convolution kernel, methods of processing noise, implementation of programs and instructions for setting the valves are described. Added functionality for entering a custom convolution kernel, processing images using Gaussian noise (σ = 25.0) and “salt-pepper” type noise (5% neutrality), with the further possibility of updating the image by resetting the noise. The program allows you to use convolution kernels with any weight coefficients. The program uses the PSNR criterion to evaluate the luminosity of image processing.Given the widespread use of convolutional filters in computer vision and digital signal processing, it is an important task to demonstrate and quantify their effectiveness. To solve this problem, we developed a computer program that compares different convolutional filters (sharpening, blurring, edge detection, embossing, and an eigenfilter) for image processing. The quality of processing is evaluated using the peak signal-to-noise ratio (PSNR) between the original and processed image.