{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:07:32Z","timestamp":1769576852264,"version":"3.49.0"},"reference-count":34,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In pharmaceutical manufacturing, tablets experience flaws like half\u2010grain tablets, multi\u2010pill tablets and paste tablets as a result of breaking, adhesion or inadequate pressing. The packaging material, typically composed of dual aluminium foil, exhibits high reflectivity. This reduces the overall image contrast and weakens the feature differences between defective areas and the background. This phenomenon significantly affects the accuracy of defect detection in pharmaceutical quality control. To address the above problems, this paper proposes a contrast enhancement method called Wavelet Quadtree Contrast Limited Adaptive Histogram Equalisation Tablet Enhancement (WCTE) for tablet images. The method combines Haar Wavelet Transform and CLAHE with adaptive quadtree chunking to enhance the detection accuracy of low\u2010contrast tablets. The proposed approach uses Haar wavelets to decompose tablet images at multiple scales. It then applies power transform enhancement and soft\u2010threshold denoising to sub\u2010bands of different frequencies, highlighting edge details and suppressing background interference. The image is reconstructed by the inverse wavelet transform. To avoid edge artefacts and local over\u2010enhancement from fixed blocks, an adaptive quadtree CLAHE strategy driven by local variance is applied. This allows adaptive segmentation of enhancement regions and ensures smooth transitions. The YOLOv11 model is utilised to identify the target in the augmented tablet image, facilitating the precise classification of typical flaws such as half\u2010grain tablets, multi\u2010pill tablets and paste tablets. Experimental results show that WCTE\u2010enhanced images outperform both traditional CLAHE and unenhanced ones in entropy, contrast, clarity and average gradient. The comprehensive scores improve by 27.15% and 11.42% relative to the original and CLAHE images, respectively. The YOLOv11 model demonstrates a significant enhancement in defect detection accuracy for WCTE\u2010enhanced images, with improvements of 13.33% and 9.06% for half\u2010grain tablets and paste\u2010like defects, respectively. The accuracy for multi\u2010pill defects remains stable, while the overall mean average precision (mAP) increases by 2.5%, and the false detection rate decreases by 9%. This improvement further substantiates the efficacy and applicability of this method in low\u2010contrast target detection tasks.<\/jats:p>","DOI":"10.1049\/ipr2.70293","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T15:57:36Z","timestamp":1769529456000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wavelet Quadtree Contrast Limited Adaptive Histogram Equalisation Tablet Enhancement Method for Defect Detection in Low\u2010Contrast Tablets"],"prefix":"10.1049","volume":"20","author":[{"given":"Zimei","family":"Tu","sequence":"first","affiliation":[{"name":"School of Intelligent Manufacturing and Control Engineering Polytechnic University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6059-1259","authenticated-orcid":false,"given":"Qinna","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Control Engineering Polytechnic University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Control Engineering Polytechnic University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhua","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Control Engineering Polytechnic University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0734\u2010189X(87)80186\u2010X"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132312649"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","unstructured":"S.Muminthaj S.Kayalvizhi S.Krishnakumar M.Preethi andL.Prabhurajan \u201cImplementation of an Image Defogging Algorithm Using Wavelet Transform \u201d inInternational Conference on Innovation Sustainability and Applied Sciences (Springer Nature Switzerland 2023) 937\u2013943.","DOI":"10.1007\/978-3-031-68952-9_121"},{"issue":"6","key":"e_1_2_12_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3618373","article-title":"Low\u2010Light Image Enhancement With Wavelet\u2010Based Diffusion Models","volume":"42","author":"Jiang H.","year":"2023","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111594"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","unstructured":"H.Jin J.Wang F.Zuo et\u00a0al. \u201cDiffusion Model With Multi\u2010Layer Wavelet Transform for Low\u2010Light Image Enhancement \u201d inICASSP 2025\u20102025 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (IEEE 2025) 1\u20135.","DOI":"10.1109\/ICASSP49660.2025.10888098"},{"key":"e_1_2_12_8_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpharm.2024.124896","article-title":"Automated Tablet Defect Detection and the Prediction of Disintegration Time and Crushing Strength With Deep Learning Based on Tablet Surface Images","volume":"667","author":"Di\u00f3szegi A.","year":"2024","journal-title":"International Journal of Pharmaceutics"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25175254"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/jpbi2020009"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.104481"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102234"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2022.168899"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831\u2010022\u201009755\u20102"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging10010028"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109050"},{"key":"e_1_2_12_17_1","doi-asserted-by":"crossref","unstructured":"J.ShiandQ.Sang \u201cBSDiff: Low\u2010Light Image Enhancement via Blueprint Separable Convolution and Wavelet\u2010Diffusion Model \u201d inChinese Conference on Pattern Recognition and Computer Vision (PRCV) (Springer Nature Singapore 2024) 369\u2013382.","DOI":"10.1007\/978-981-97-8685-5_26"},{"key":"e_1_2_12_18_1","first-page":"1323","article-title":"Enhanced Satellite Image Clarity Using Stationary Wavelet Transform and Singular Value Decomposition","volume":"4","author":"Kumari G. 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