Optimizasyon yöntemlerinin süper çözünürlük üzerine katkı analizi

In this study, the benefits of choosing a robust optimization function with super resolution are analyzed. For this purpose, the different optimizers are included in the simple Convolutional Neural Network (CNN) architecture SRNET, to reveal the performance of the each method. Findings of this research provides that Adam and Nadam optimizers are robust when compared to (Stochastic Gradient Descent) SGD, Adagrad, Adamax and RMSprop. After experimental simulations, we have achieved the 35.91 (dB)/0.9960 and 35.97 (dB)/0.9961 accuracy rates on Set5 images from Adam and Nadam optimizers, respectively (9-1-5 network structure and filter sizes 128 and 64). These results show that selected optimization function for the CNN model plays an important role in increasing the accuracy rate in the super-resolution problem.Bu çalışmada, süper çözünürlükte sağlam bir optimizasyon fonksiyonu seçmenin yararları analiz edilmiştir. Bu amaçla her yöntemin performansını ortaya çıkarmak için farklı optimize ediciler, basit Evrişimsel Sinir Ağı (CNN) mimarisi SRNET' e dahil edilmiştir. Bu araştırmanın bulguları, Adam ve Nadam optimize edicilerin Stokastik Gradyan İnişi (SGD), Adagrad, Adamax ve RMSprop ile karşılaştırıldığında daha kararlı olduğunu göstermektedir. Deneysel simülasyonlardan sonra, Adam ve Nadam optimize edicilerinden Set5 görüntülerinde sırasıyla 35.91 (dB)/0.9960 ve 35.97 (dB)/0.9961 doğruluk oranlarına ulaştık (9-1-5 ağ yapısı ve filtre boyutları 128 ve 64). Bu sonuçlar, CNN modeli için seçilen optimizasyon fonksiyonunun süper çözünürlük probleminde doğruluk oranını arttırmada önemli bir rol oynadığını göstermektedir

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution

In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parameter-economic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multi-supervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.Comment: Accepted by IEEE Transactions on Image Processing (IEEE-TIP

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included