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In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network's ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted. [ABSTRACT FROM AUTHOR]