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This paper investigates camera phone image quality, namely the effect of sensor megapixel (MP) resolution on the perceived quality of images displayed at full size on high-quality desktop displays. For the purpose, we use images from simulated cameras with different sensor MP resolutions. We employ methods recommended in the IEEE 1858 Camera Phone...
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Linear complementary dual (LCD) codes are linear codes which intersect their dual codes trivially, which have been of interest and extensively studied due to its wide applications. In this paper, we give some methods for constructing LCD codes over small fields by modifying some known methods. We show that all odd-like binary LCD codes, ternary LCD...
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Citations
... The images in our dataset were acquired by collecting samples and capturing images directly from the different fields of While many researchers use several accessible image datasets for research purposes, such as the "PlantVillage" image dataset, the APS image dataset, and so on 27 , our image dataset consisted wholly of primary material. It is considered that the megapixels of the camera and its orientation can affect the quality of the captured images 28 . To ensure the model could learn from detailed visual information, we prioritized capturing high-quality images for our primary dataset. ...
Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected by red rust, brown blight, grey blight, and healthy leaves. Afterward, the model’s prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry.
Thanks to their portability, connectivity, and their image performance – which is constantly improving – smartphone cameras (SPCs) have been people’s loyal companions for quite a while now. In the past few years, multicamera systems have become well and truly established, alongside 3D acquisition systems such as time-of-flight (ToF) sensors. This article looks at the evolution and status of SPC imaging technology. After a brief assessment of the SPC market and supply chain, the camera system and optical image formation is described in more detail. Subsequently, the basic requirements and physical limitations of smartphone imaging are examined, and the optical design of state-of-the-art multicameras is reviewed alongside their optical technology and manufacturing process. The evolution of complementary metal oxide semiconductor (CMOS) image sensors and basic image processing is then briefly summarized. Advanced functions such as a zoom, shallow depth-of-field portrait mode, high dynamic range (HDR), and fast focusing are enabled by computational imaging. Optical image stabilization has greatly improved image performance, enabled as it is by built-in sensors such as a gyroscope and accelerometer. Finally, SPCs’ connection interface with telescopes, microscopes, and other auxiliary optical systems is reviewed.
Zusammenfassung
Die Sensoren in Smartphonekameras sind prinzipiell vergleichbar aufgebaut wie in großen Kameras. Die geringen Abmessungen erzwingen jedoch sehr kleine Sensoren mit winzigen Pixeln. Das wirkt sich ungünstig auf den Dynamikbereich und das Rauschverhalten aus. Um trotzdem Aufnahmen zu erhalten, die zumindest auf den Smartphonedisplays gut aussehen, wird eine aufwendige Bildbearbeitung eingesetzt. Dabei spielen Multi‐Shot‐Aufnahmen eine wichtige Rolle: Die Kamera nimmt mehrere Bilder auf und verrechnet diese. Das erweitert den aufnahmeseitigen Dynamikbereich (High Dynamic Range, HDR) und reduziert das Bildrauschen. Die Software gleicht sogar geringe Verschiebungen in den Einzelbildern aus, die durch Bewegungen verursacht werden. Allerdings kommt das Verfahren bei schnell bewegten Motiven und wenig Licht an Grenzen. Im Portraitmodus nutzt die Software Informationen aus mehreren Kamerabildern sowie neuerdings Laufzeitmessungen eines Time‐of‐Flight‐Sensors, um das Motiv vor einem künstlich unscharf gerechneten Hintergrund freizustellen. Der Preis sind Bildfehler, die aber auf kleinen Displays kaum auffallen.
