TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder

Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform are employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, since texture defects often exhibit characteristic changes in specific frequency ranges, analyzing the frequency domain enables effective defect detection. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with existing approaches.

PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

The objective of a style transfer is to maintain the content of an image while transferring the style of another image. However, conventional research on style transfer has a significant limitation in preserving facial landmarks, such as the eyes, nose, and mouth, which are crucial for maintaining the identity of the image. In Korean portraits, the majority of individuals wear "Gat", a type of headdress exclusively worn by men. Owing to its distinct characteristics from the hair in ID photos, transferring the "Gat" is challenging. To address this issue, this study proposes a deep learning network that can perform style transfer, including the "Gat", while preserving the identity of the face. Unlike existing style transfer approaches, the proposed method aims to preserve texture, costume, and the "Gat" on the style image. The Generative Adversarial Network forms the backbone of the proposed network. The color, texture, and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16, and only the necessary elements during training were preserved using a facial landmark mask. The head area was presented using the eyebrow area to transfer the "Gat". Furthermore, the identity of the face was retained, and style correlation was considered based on the Gram matrix. The proposed approach demonstrated superior transfer and preservation performance compared to previous studies.

Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator

When a JPEG image is compressed using the loss compression method with a high compression rate, a blocking phenomenon can occur in the image, making it necessary to restore the image to its original quality. In particular, restoring compressed images that are unrecognizable presents an innovative challenge. Therefore, this paper aims to address the restoration of JPEG images that have suffered significant loss due to maximum compression using a GAN-based net-work method. The generator in this network is based on the U-Net architecture and features a newly presented hourglass structure that can preserve the charac-teristics of deep layers. Additionally, the network incorporates two loss functions, LF Loss and HF Loss, to generate natural and high-performance images. HF Loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features, which can enhance performance for the high-frequency region. LF Loss, on the other hand, is used to handle the low-frequency region. These two loss functions facilitate the generation of images by the generator that can deceive the discriminator while accurately generating both high and low-frequency regions. The results show that the blocking phe-nomenon in lost compressed images was removed, and recognizable identities were generated. This study represents a significant improvement over previous research in terms of image restoration performance.

Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on CNN, but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through Grabcut. It shows high performance through image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images except for the actual input as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed higher performance than previous researches, and image-based scores showed the best performanc