![]() Random Forest algorithm has been employed for both classification and prediction. An in‐house database prepared with images of pure and adulterated turmeric powder samples has been used for experimentation. The scope has been remained to screening of Sudan dye‐I adulteration in turmeric powder. This work reports a new computer vision framework, which can simultaneously detect the presence of adulteration and predict the possible percentage of adulteration addressing the stated limitations. ![]() The existing adulteration detection processes are largely instrumental and analytical with high accuracy but include limitations like long testing time, expensiveness, and lack of mobility. ![]() Non‐destructive turmeric adulteration detection is a challenging research area. The results showed that the proposed method is better than these fusion methods in terms of focus and quality.Īdulterants can cause different health hazards upon prolonged consumption, but it is difficult to detect with human eyes. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The focusing filter consists of a combination of two filters, which are a Wiener filter and a sharpening filter. In this paper, a fusion method is proposed to increase the focus of the fused image and to achieve the highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. However, they did not take into account the focus of the image. Most available image fusion algorithms are superior in results. Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field.
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