The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling’s T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling’s T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine.

https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-29/issue-4/041013/Adversarial-autoencoder-for-detecting-anomalies-in-soldered-joints-on-printed/10.1117/1.JEI.29.4.041013.pdf

Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

Contrast Enhancement of Retinal Images Using Green Plan Masking and Whale Optimization Algorithm

We propose a defect detection method of solders on a printed circuit board using X-ray CT inspection system and Adversarial Autoencoder (AAE)[1] . We obtain sliced images of the solder using X-ray CT and extract their features that follow the standard normal distribution by using AAE. Then, the solder defects are detected by Hotelling's T square[2]. As a result of experiments, we show that we can classify normal and anomalous data samples completely on the condition of training with large normal samples and small anomalous samples.

Anomaly detection of solder joint on print circuit board by using Adversarial Autoencoder

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Histogram eşitleme dijital görüntülerin kontrastını artırmak için kullanılan yöntemlerden biridir. İdeal histogram eşitleme yöntemlerinde, girdi ve çıktı arasındaki görüntü benzerliğini koruyarak histogramdaki dağılımları tekdüze hale getirerek kontrast gerilmektedir. Frekans alanında yapılan bu çalışmada, Düşük Dinamik Aralığında değiştirilmiş ayrık Haar Dalgacık Dönüşümü ile yeni bir görüntü kontrast germe yöntemi önerilmiştir. Bu yöntemde Olasılık Kütle Fonksiyonunu ile frekansların yüksek geçiş kanalında gürültülü frekanslara bir baskılama işlemi gerçekleştirilmiştir. Daha sonra yapılan frekans dönüşümlerinde histogram frekansların dinamik aralıklarında önemli bir azalma sağlanmıştır. Frekans alanındaki bu işlem görüntüde standart sapmanın artmasını sağlayarak görüntü kalitesinin iyileşmesini sağlar. Kıyaslamalı bir veri seti üzerinde yapılan deneysel çalışmalarda, önerilen yöntem konvansiyonel metotlarla kıyaslanmış ve umut verici sonuçlar elde edilmiştir. Görüntü kalitesi değerlendirme metriklerinden Tepe Sinyal Gürültü Oranı (PSNR), Ortalama Kare Hata (MSE), Yapısal Benzerlik Endeks Ölçütü (SSIM) ve Kontrast değeri deneysel çalışmalarda kullanılmıştır. Önerilen bu yaklaşım ile elde edilen sonuçlar diğer algoritmaların sonuçları ile kıyaslandığında hem kalitatif hem de kantitatif açıdan başarılı bulunmuştur. 

/pdf/degistirilmis-ayrik-haar-dalgacik-donusumu-ile-yeni-bir-3ayfy80k.pdf

Deği̇şti̇ri̇lmi̇ş ayrik haar dalgacik dönüşümü i̇le yeni̇ bi̇r hi̇stogram eşi̇tleme yöntemi̇

Traditional histogram equalization may cause degraded results of over-enhanced images under uneven illuminations. In this paper, a simple and effective image contrast enhancement method is proposed to achieve high dynamic range imaging. First, the illumination of each pixel is estimated by using an induced norm of a patch of the image. Second, a pre-gamma correction is proposed to enhance the contrast of the illumination component appropriately. The parameters of gamma correction are set dynamically based on the local patch of the image. Third, an automatic Contrast-Limited Adaptive Histogram Equalization (CLAHE) whose clip point is automatically set is applied to the processed image for further image contrast enhancement. Fourth, a noise reduction algorithm based on the local patch is developed to reduce image noise and increase image quality. Finally, a post-gamma correction is applied to slightly enhance the dark regions of images and not affect the brighter areas. Experimental results show that the proposed method has its superiority over several state-of-the-art enhancement quality techniques by using qualitative and quantitative evaluations.

/pdf/icebin-image-contrast-enhancement-based-on-induced-norm-and-3n6hvxet44.pdf

ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches

Head-In-Pillow (HIP) is a solder ball defect. The defect can be caused by surface of solder ball oxidation, poor wetting of the solder, or by distortion of the Printed Circuit Board (PCB) by the heat of the soldering process. The current diagnosis of the HIP defects is difficult to find out the problems, and some destructive tests are not recommended for use. In this paper, we use different angles of 2D X-Rays images to reconstruct the 3D PCB volumetric data. We crop the 3D solder balls volumetric data to 46 × 46 × 46 pixels from the 3D PCB model. Because HIP problems do not happen often, we use the data augmentation method to expand our solder ball data. We propose a new 3D Convolutional Neural Network (CNN) to inspect the HIP problems. Our network uses convolutional blocks that consist of different convolutional paths and the dense connectivity method to connect the blocks. The network can learn various features through these convolutional blocks with different convolutional paths. Moreover, the features of each layer will be fully utilized in the following layers by the dense connectivity method, and also avoid some features lost in a deep convolutional path. In the last layer of our network, the global average pooling can let our network process more different sizes of solder ball data, and the normalized same size vector can be used to do the end-to-end learning. Compared with other classic models, our network not only has fewer parameters but also has faster training time.

Use 3D Convolutional Neural Network to Inspect Solder Ball Defects

We aim to reconstruct the 3D model of solder balls on the PCB (Printed Circuit Board) according to many projections of X-ray images of those solder balls. We mainly use a tomographic reconstruction algorithm FBP (Filtered Back Projection) to obtain the cross-section of different levels without breaking the PCB and inspect their inner structure. We also optimize our execution times and compare our results with Volume Graphics.

Solder Ball 3D Reconstruction with X-Ray Images Using Filtered Back Projection

This paper presents a watercolor rendering technique that converts photographic images into watercolor painting. An image diffusion and mask filter algorithm are proposed. A heat transfer equation is used as the mage diffusion method and a statistic mask filter is applied to the photographic image processed by the heat transfer equation previously. Experimental results show that better color effect with natural appearance is achieved.

Tzu-Chia Tung

Papers

ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches

Use 3D Convolutional Neural Network to Inspect Solder Ball Defects

Solder Ball 3D Reconstruction with X-Ray Images Using Filtered Back Projection

Image watercolorization based on color intensity alteration